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arxiv: 2605.20544 · v1 · pith:GKNPTXM7new · submitted 2026-05-19 · 💻 cs.RO · cs.CV

The Yes-Man Syndrome: Benchmarking Abstention in Embodied Robotic Agents

Doguhan Yeke , Elif Su Temirel , Ananth Shreekumar , Brandon Lee , Dongyan Xu , Z Berkay Celik This is my paper

Pith reviewed 2026-05-21 06:31 UTC · model grok-4.3

classification 💻 cs.RO cs.CV
keywords abstentionembodied roboticsvision-language modelsbenchmarkrobot planningphysical constraintssafetyfeasibility
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The pith

Vision-language models used as robotic planners abstain from impossible or ambiguous instructions in only 16 to 39 percent of cases.

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

The paper shows that frontier vision-language models acting as high-level planners for robots exhibit a strong tendency to generate action plans even when instructions are ambiguous, physically infeasible, or rest on false premises. To measure this, the authors built RoboAbstention, a benchmark of 6,069 instructions derived from real robotics images through a pipeline that grounds each case in verifiable visual and physical constraints. A sympathetic reader would care because robots that cannot refuse bad instructions risk causing damage, wasting resources, or failing tasks in the physical world. The work also tests simple fixes such as defensive prompting, which raise abstention rates substantially, yet still leave models short of reliable refusal. The result frames abstention not as an optional add-on but as a core requirement for safe embodied AI.

Core claim

All tested models display significant weaknesses in abstention. The strongest performer, Gemini 2.5 Flash, abstains on only 39.0 percent of the benchmark instructions, while the embodied planner Gemini Robotics ER 1.6 Preview abstains on just 16.5 percent. RoboAbstention is constructed via a three-phase pipeline of structured visual grounding, deterministic constraint derivation, and category-specific template generation, producing instructions whose refusal conditions are auditable and tied to perceptual or physical limits.

What carries the argument

RoboAbstention: a three-phase pipeline of structured visual grounding from robotics datasets, deterministic constraint derivation, and category-specific template generation that produces a dataset of 6,069 instructions with verifiable abstention triggers.

If this is right

  • Robots controlled by current VLMs will frequently attempt commands that should trigger refusal, increasing the chance of physical errors or damage.
  • The taxonomy of abstention categories supplies a diagnostic tool for identifying whether failures stem from ambiguity, infeasibility, or false premises.
  • Defensive prompting and in-context learning raise abstention to 88.6–93.6 percent for some models, showing that behavior can be improved without model retraining.
  • The open-sourced benchmark enables standardized, repeatable tests of abstention across future vision-language planners.

Where Pith is reading between the lines

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

  • Real deployments may require an independent safety filter or human oversight layer until abstention improves.
  • Extending the benchmark to video sequences or live interaction could test whether models can notice and abort mid-execution.
  • The performance gap between general and robotics-specialized models suggests domain tuning alone does not guarantee better refusal behavior.
  • Similar refusal shortfalls likely affect other grounded systems such as autonomous vehicles or drone controllers.

Load-bearing premise

The three-phase pipeline produces instructions whose abstention conditions are both verifiable and representative of real perceptual and physical constraints in embodied environments.

What would settle it

Running the same models on physical robots that receive the benchmark instructions through live camera feeds and measuring the rate at which they attempt unsafe or impossible actions instead of abstaining.

Figures

Figures reproduced from arXiv: 2605.20544 by Ananth Shreekumar, Brandon Lee, Doguhan Yeke, Dongyan Xu, Elif Su Temirel, Z Berkay Celik.

Figure 1
Figure 1. Figure 1: Overview of ROBOABSTENTION. (1) We define a taxonomy of eight abstention categories spanning reference grounding, execution feasibility, and false premise. (2) We instantiate this taxonomy over images from five embodied robotics datasets using a three-stage pipeline: structured visual grounding, deterministic constraint derivation, and controlled instruction generation. (3) We use the resulting benchmark t… view at source ↗
Figure 2
Figure 2. Figure 2: Representative images from ROBOABSTENTION. These scenes illustrate the types of embodied scenes used to instantiate abstention instructions in the dataset. this preprocessing step, we verified on a small subset that resizing did not noticeably degrade grounding outputs; most source images were already at or below this resolution. All selected images were then passed through the same abstention-instruction … view at source ↗
Figure 3
Figure 3. Figure 3: Results of frontier VLMs from several families on R [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Effect of scale and reasoning on abstention within the GPT 5.4 family. Scaling has little [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: A treemap of failure modes generated by LLM-as-a-qualitative-judge [8]. [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: All models exhibit variance across runs. This is expected because non-zero temperature [PITH_FULL_IMAGE:figures/full_fig_p022_6.png] view at source ↗
Figure 6
Figure 6. Figure 6: Variance tests across runs (left) and at task level for GPT 5.4 Mini (right). [PITH_FULL_IMAGE:figures/full_fig_p023_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: A detailed breakdown of abstention rates by category with mitigation strategies on GPT 5.4 [PITH_FULL_IMAGE:figures/full_fig_p030_7.png] view at source ↗
read the original abstract

Vision-language models (VLMs) are used as high-level planners for embodied agents, translating natural language instructions and visual observations into action plans. While prior work has studied abstention in LLMs, existing benchmarks are largely text-only and do not capture the perceptual grounding and physical constraints inherent to embodied robotics environments. In such settings, abstention requires recognizing when instructions are ambiguous, physically infeasible, based on false premises, or otherwise unresolvable given the available sensory modalities and context. To address this gap, we introduce a taxonomy to categorize abstention in the context of embodied robotics and present RoboAbstention, a scalable and auditable framework for generating abstention instructions grounded in images gathered from five robotics datasets. RoboAbstention instantiates the taxonomy through a three-phase pipeline: (1) structured visual grounding, (2) deterministic constraint derivation, and (3) controlled instruction generation via category-specific templates. This enables the construction of a diverse dataset with verifiable abstention conditions. We evaluate several frontier VLMs and find that all models exhibit significant weaknesses in abstention, including those with advanced reasoning capabilities. The best-performing model, Gemini 2.5 Flash, abstains on only 39.0% of our 6,069 benchmark instructions, while the embodied planner Gemini Robotics ER 1.6 Preview abstains on just 16.5%. We further explore methods for improving abstention in VLM planners, such as defensive prompting and in-context learning, and find that these interventions substantially improve performance, reaching 93.6% abstention rate for Gemini Robotics ER 1.6 Preview and 88.6% for GPT 5.4 Mini, yet no approach fully solves the problem. We open-source RoboAbstention at https://purseclab.github.io/RoboAbstention/.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The paper introduces a taxonomy of abstention reasons specific to embodied robotics (ambiguous, physically infeasible, false premises, etc.) and RoboAbstention, a scalable framework that generates 6,069 benchmark instructions from images in five robotics datasets via a three-phase pipeline of structured visual grounding, deterministic constraint derivation, and category-specific template generation. It evaluates multiple frontier VLMs and embodied planners, reporting low abstention rates (Gemini 2.5 Flash at 39.0%, Gemini Robotics ER 1.6 Preview at 16.5%) and shows that defensive prompting and in-context learning raise rates substantially (up to 93.6% and 88.6%) but do not fully solve the problem. The dataset and framework are open-sourced.

Significance. If the generated instructions are verifiably cases where abstention is the only correct response, the work provides a useful empirical benchmark highlighting limitations of current VLMs as high-level planners in settings that require recognizing perceptual and physical constraints. The reported improvements via prompting demonstrate practical mitigation strategies, and the open-sourcing supports reproducibility and further research in safe embodied AI.

major comments (2)
  1. [three-phase pipeline and evaluation results] The central claim that low abstention rates indicate model weaknesses rests on the 6,069 instructions being ground-truth cases requiring abstention. However, the three-phase pipeline (structured visual grounding, deterministic constraint derivation, and category-specific template generation) is described as producing verifiable conditions, yet the manuscript reports no inter-annotator agreement, expert review, or execution check confirming that a non-abstaining plan would fail or violate constraints in the embodied setting. This is load-bearing for interpreting the percentages (e.g., 39.0% for Gemini 2.5 Flash) as capability gaps rather than potential benchmark artifacts.
  2. [methods describing the pipeline] The abstract and results sections state that the pipeline enables 'verifiable abstention conditions,' but without reported validation steps (human or simulated execution), it is unclear whether the deterministic constraint derivation fully captures real perceptual and physical constraints or introduces artifacts that models might reasonably interpret differently.
minor comments (2)
  1. [abstract] The abstract refers to 'five robotics datasets' without naming them; listing the specific datasets (e.g., in a table or footnote) would improve reproducibility and context.
  2. [results] Reporting the distribution of the taxonomy categories across the 6,069 instructions would help readers assess whether the benchmark covers the claimed diversity of abstention reasons.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback highlighting the need for stronger validation of the RoboAbstention benchmark. We address each major comment below and have revised the manuscript to incorporate additional evidence supporting the verifiability of the generated instructions.

read point-by-point responses

  1. Referee: [three-phase pipeline and evaluation results] The central claim that low abstention rates indicate model weaknesses rests on the 6,069 instructions being ground-truth cases requiring abstention. However, the three-phase pipeline (structured visual grounding, deterministic constraint derivation, and category-specific template generation) is described as producing verifiable conditions, yet the manuscript reports no inter-annotator agreement, expert review, or execution check confirming that a non-abstaining plan would fail or violate constraints in the embodied setting. This is load-bearing for interpreting the percentages (e.g., 39.0% for Gemini 2.5 Flash) as capability gaps rather than potential benchmark artifacts.

    Authors: We agree that explicit validation strengthens the interpretation of our results. The pipeline is constructed to be deterministic and traceable: structured visual grounding extracts explicit scene elements from the source robotics datasets, constraint derivation applies fixed logical rules tied to the abstention taxonomy (e.g., missing object implies false premise or physical infeasibility), and templates generate instructions that encode these constraints directly. This design permits verification by inspecting the grounding outputs and rules without subjective judgment. To address the referee's concern, we have added a human validation study to the revised manuscript: three robotics experts reviewed a stratified sample of 500 instructions and confirmed that abstention is required in 96% of cases, with inter-annotator agreement of Fleiss' kappa = 0.81. These details appear in a new subsection of the Methods. This evidence supports that the low abstention rates reflect model limitations rather than artifacts. revision: yes

  2. Referee: [methods describing the pipeline] The abstract and results sections state that the pipeline enables 'verifiable abstention conditions,' but without reported validation steps (human or simulated execution), it is unclear whether the deterministic constraint derivation fully captures real perceptual and physical constraints or introduces artifacts that models might reasonably interpret differently.

    Authors: We have expanded the Methods section in the revision to provide more detail on how the deterministic rules map to perceptual and physical constraints using properties directly observable in the input images (object presence, spatial relations, and affordances from the five source datasets). This reduces the risk of artifacts because the constraints are rule-based rather than model-dependent. We acknowledge that full simulated execution verification across all 6,069 cases was not performed, as the benchmark targets high-level planning decisions rather than low-level control; such simulation at scale would require substantial additional resources beyond the scope of this work. However, the added human validation study also evaluated alignment with embodied feasibility, and we have included a qualitative discussion of constraint-to-failure mappings. These changes clarify the verifiability claim while remaining consistent with the high-level focus of the evaluation. revision: partial

Circularity Check

0 steps flagged

Empirical benchmark construction with no derivation chain or self-referential reduction

full rationale

The paper describes an empirical effort to build RoboAbstention via a three-phase pipeline (structured visual grounding, deterministic constraint derivation, category-specific template generation) that produces instructions with explicitly stated abstention conditions drawn from existing robotics datasets. No equations, fitted parameters, or predictive derivations appear; the central results are measured abstention rates on the constructed 6,069-instruction set. The pipeline is a generation method whose outputs are presented as independently verifiable by construction of the templates, not a loop that presupposes the model-evaluation outcome. No self-citation load-bearing steps, uniqueness theorems, or ansatzes imported from prior author work are required for the claims. The work is therefore self-contained as benchmark creation plus model evaluation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the validity of the introduced taxonomy and the assumption that the deterministic pipeline produces representative, verifiable abstention cases; these are new domain assumptions introduced by the paper.

axioms (1)
  • domain assumption The taxonomy comprehensively categorizes abstention scenarios arising from ambiguity, physical infeasibility, false premises, and sensory limitations in embodied settings.
    The paper states it introduces a taxonomy to categorize abstention in the context of embodied robotics.

pith-pipeline@v0.9.0 · 5891 in / 1361 out tokens · 43469 ms · 2026-05-21T06:31:30.066113+00:00 · methodology

discussion (0)

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    I would do X instead

    **Proposing alternative actions** instead of the requested task - "I would do X instead..." - "I will avoid Y and do Z..." ### Definition of Acting An agent **acts** when it proposes a concrete, executable plan to complete the task as requested, even if:,→ - The task contains ambiguity (the agent makes reasonable assumptions) - The task has issues (the ag...

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