arxiv: 2605.11223 · v1 · submitted 2026-05-11 · 💻 cs.AI

Recognition: 2 theorem links

· Lean Theorem

Do Vision-Language-Models show human-like logical problem-solving capability in point and click puzzle games?

Dominik Helfenstein , Marco Menner , Maximilian Triebel

Authors on Pith no claims yet

Pith reviewed 2026-05-13 01:55 UTC · model grok-4.3

classification 💻 cs.AI

keywords vision-language modelsbenchmarkpuzzle solvinglogical reasoningvisual groundinginteractive environmentsphysics puzzles

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The pith

Vision-language models plan solutions to physics puzzles but cannot execute the precise mouse clicks needed to finish them.

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

The paper introduces a benchmark called VLATIM built around the classic game The Incredible Machine 2 to test whether vision-language models can solve point-and-click puzzles with human-like logical reasoning. The benchmark splits the task into five stages that move from basic image understanding to full multi-step puzzle completion. Tests on current models show strong performance on high-level planning yet consistent failure on the visual precision and continuous mouse control required for actual play. This separation between reasoning and execution leads the authors to conclude that the models lack complete human-like problem-solving ability in these environments.

Core claim

Large proprietary vision-language models demonstrate superior planning abilities in the VLATIM benchmark yet struggle with precise visual grounding and continuous mouse interactions required for full puzzle solutions, leading to the conclusion that they do not yet exhibit human-like logical problem-solving capabilities.

What carries the argument

The VLATIM benchmark, a five-part progressive evaluation that measures the gap between high-level logical reasoning and precise execution in point-and-click physics puzzles.

Load-bearing premise

That failure to perform precise mouse interactions in this game benchmark means the models lack human-like logical problem-solving capability overall.

What would settle it

A model that completes the full set of VLATIM puzzles at or above typical human success rates would show the central claim is not correct.

Figures

Figures reproduced from arXiv: 2605.11223 by Dominik Helfenstein, Marco Menner, Maximilian Triebel.

**Figure 1.** Figure 1: Screenshot of a puzzle in TIM for a manipulation task with the action history generated by the model, indicated by the red dots and green lines. 3.8. Part 5: Full Puzzle Solving Full puzzle solving combines all capabilities evaluated in the previous parts into the final goal of solving puzzles. The actual puzzle objective can be found in the goal description in the game. In the prompt, the model is only pr… view at source ↗

**Figure 2.** Figure 2: displays the classification and multi-/localization scores achieved by each of the five evaluated models. It shows missing multi-/localization scores for UI-Tars, as it is not able to output bboxes. The model outputs single point coordinates, despite trying different prompts, i.e. asking the model to draw a rectangle around an object with the mouse drag action, which leads us to the conclusion that UI-Tars… view at source ↗

**Figure 3.** Figure 3: Distances between bounding boxes Part 1 visual grounding. This leads us to the conclusion that Gemini and GPT can identify objects but are not capable of drawing precise bboxes, while the opposite holds true for Qwen2.5 and Qwen3. Part 2: Domain Understanding [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 6.** Figure 6: and [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗

**Figure 7.** Figure 7: Distances of visual evaulation Part 3 event reasoning. the complexities of handle manipulation and object deformation. Consequently, only UI-Tars and Qwen3 managed “Stretch”, while Qwen3 and Gemini managed “Rotate”, all at very low success rates. No model successfully completed the “Multi” task. Even though Qwen3 is generally able to solve all tasks, it fails to demonstrate the reliability necessary to c… view at source ↗

**Figure 8.** Figure 8: Success rates of Part 4 manipulation. Part 5: Full puzzle solving As foreshadowed by the results in the Multi category of Part 4, none of the models were able to successfully solve a complete puzzle. Despite testing across multiple levels of varying difficulty, every model failed. Nevertheless, especially with this category we were able to gain a lot of qualitative findings. Final scores [PITH_FULL_IMAGE… view at source ↗

**Figure 9.** Figure 9: TIM game interface with playfield in the middle (blue area), parts bin as well as navigation buttons on the right and task description at the bottom. D. Action Space • Move: "Move [object] to [location]." - Objects can be moved within the playfield. The placeholder [location] is replaced with a verbal description of a location using reference points like e.g. objects or Heads-Up Display (HUD) elements. • P… view at source ↗

read the original abstract

Vision-Language(-Action) Models (VLMs) are increasingly applied to interactive environments, yet existing benchmarks often overlook the complex physical reasoning required for point-and-click puzzle games. This paper introduces Vision-Language Against The Incredible Machine (VLATIM), a benchmark designed to evaluate human-like logical problem-solving capabilities within the classic physics puzzle game The Incredible Machine 2 (TIM). Unlike existing benchmarks, VLATIM specifically targets the critical gap between high-level logical reasoning and continuous action spaces requiring precise mouse interactions. This benchmark is structured into five progressive parts, assessing capabilities that range from basic visual grounding and domain understanding to multi-step manipulation and full puzzle solving. Our results reveal a significant disparity between reasoning and execution. While large proprietary models demonstrate superior planning abilities, they struggle with precise visual grounding. Consequently, they do not yet show human-like problem-solving capabilities.

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.

Desk Editor's Note private letter to a colleague

VLATIM is a sensible new benchmark for VLMs on point-and-click physics puzzles, but the results stay thin without numbers or human baselines.

read the letter

The paper introduces VLATIM, a five-stage benchmark built on The Incredible Machine 2, to test vision-language models on puzzles that mix logical planning with precise mouse clicks. The central observation is that larger models handle the planning steps better than the exact visual grounding and continuous actions needed to finish the tasks. That split is the main takeaway they want readers to carry away. The benchmark itself is the clearest contribution. Existing VLM tests often stay at high-level reasoning or discrete actions, so targeting the gap between thought and precise execution in a real physics game fills a practical hole. Breaking the evaluation into progressive parts—from basic object recognition up to full multi-step solving—gives a structured way to locate where models fail. That design choice is straightforward and useful for anyone trying to build agents that operate in continuous screen environments. The work stays grounded in an off-the-shelf game rather than inventing new environments from scratch, which keeps the setup reproducible. The soft spots are mostly about missing detail. The abstract and available text give no quantitative scores, no description of how the mouse actions were scored, and no human performance numbers on the same tasks. Without those, the claim that the models lack human-like problem-solving rests on a qualitative disparity that is hard to weigh. The five-part structure is logical on paper, but it is not yet clear how well each stage isolates the intended capability or whether the tasks avoid unintended shortcuts. This paper is mainly for researchers working on VLM agents and interactive benchmarks. It is worth sending for peer review because the benchmark idea is concrete and addresses a real limitation in current evaluations, even though the current results section would need more data and human comparisons to stand on its own.

Referee Report

2 major / 2 minor

Summary. The paper introduces the VLATIM benchmark, built on The Incredible Machine 2, to evaluate vision-language models on point-and-click physics puzzles. It structures evaluation into five progressive parts that isolate visual grounding, domain understanding, multi-step planning, and full puzzle solving. Experiments on proprietary and open VLMs show strong high-level planning but weak performance on precise mouse-based grounding and execution, supporting the conclusion that current models lack human-like logical problem-solving in continuous-action settings.

Significance. If the reported planning-execution disparity is robust, the work provides a useful diagnostic benchmark that separates reasoning from low-level control, a distinction often collapsed in existing VLM game benchmarks. The progressive design and focus on precise continuous actions could help prioritize research on grounding and action interfaces, with potential relevance to embodied agents.

major comments (2)

[Results / §5] The central claim that models 'do not yet show human-like problem-solving capabilities' rests on the observed planning-grounding gap, yet no human performance baselines or inter-rater agreement on the VLATIM tasks are reported. Without these (e.g., in the results section or Table X), it is unclear whether the models' grounding failures exceed typical human variance or simply reflect the benchmark's difficulty.
[Benchmark definition] §3.2–3.4: The scoring protocol for 'precise visual grounding' and mouse-click success (pixel tolerance, timeout rules, partial-credit criteria) is not fully specified. This detail is load-bearing because small interface or rendering differences could inflate the reported execution failures independently of model capability.

minor comments (2)

[Abstract] Abstract: Key quantitative results (e.g., success rates per part, model names, exact deltas between planning and execution) are omitted; adding one or two headline numbers would improve readability.
[§3] Notation: 'VLATIM' and the five-part naming are introduced without an explicit table or figure summarizing the progression; a compact overview table would aid readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on VLATIM. The comments highlight important areas for strengthening the claims and ensuring reproducibility. We address each major comment below and will revise the manuscript to incorporate the suggested improvements.

read point-by-point responses

Referee: [Results / §5] The central claim that models 'do not yet show human-like problem-solving capabilities' rests on the observed planning-grounding gap, yet no human performance baselines or inter-rater agreement on the VLATIM tasks are reported. Without these (e.g., in the results section or Table X), it is unclear whether the models' grounding failures exceed typical human variance or simply reflect the benchmark's difficulty.

Authors: We agree that human performance baselines and inter-rater agreement would provide essential context for interpreting whether the observed grounding failures exceed typical human variance. Our experiments demonstrate a consistent planning-execution disparity across multiple VLMs using the progressive task structure, but without direct human data the 'human-like' claim remains partly inferential. In the revision we will collect and report human baselines on the VLATIM tasks (including inter-rater agreement) and add a new table comparing model versus human performance to support the conclusion more rigorously. revision: yes
Referee: [Benchmark definition] §3.2–3.4: The scoring protocol for 'precise visual grounding' and mouse-click success (pixel tolerance, timeout rules, partial-credit criteria) is not fully specified. This detail is load-bearing because small interface or rendering differences could inflate the reported execution failures independently of model capability.

Authors: We apologize for the incomplete specification in the submitted version. The scoring rules are defined in §3 but lack the precise numerical thresholds needed for full reproducibility. We will expand §3.4 with an explicit protocol: click success requires the mouse position to be within a 15-pixel radius of the target center, actions are timed out after 45 seconds, and partial credit is awarded proportionally to proximity (0–100% based on distance) plus correctness of the chosen object. We will also include pseudocode and example screenshots to eliminate ambiguity from rendering or interface variations. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces the VLATIM benchmark with five progressive parts to isolate visual grounding, domain understanding, manipulation, and full puzzle solving in The Incredible Machine 2. It evaluates existing VLMs on this benchmark and reports empirical performance gaps between planning and precise execution. No equations, parameter fitting, self-definitional reductions, or load-bearing self-citations appear in the derivation. The central claim follows directly from benchmark results without renaming known patterns or smuggling ansatzes via prior work. The evaluation is self-contained and externally falsifiable against model outputs on the new tasks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that VLATIM tasks validly capture human-like logical problem-solving and that execution failures indicate lack of overall capability.

axioms (1)

domain assumption VLATIM benchmark tasks measure human-like logical problem-solving capability
The paper positions the benchmark as assessing human-like capabilities without providing validation against human performance data in the abstract.

invented entities (1)

VLATIM benchmark no independent evidence
purpose: To evaluate VLMs on visual grounding, domain understanding, multi-step manipulation, and full puzzle solving in The Incredible Machine 2
Newly introduced in this paper as the core contribution.

pith-pipeline@v0.9.0 · 5447 in / 1252 out tokens · 46425 ms · 2026-05-13T01:55:25.480052+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

VLATIM benchmark structured into five progressive parts... models demonstrate superior planning abilities, they struggle with precise visual grounding.
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Action space consists of five actions... click, hover, drag, wait, finished

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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