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REVIEW 3 major objections 4 minor 20 references

Don't Let Me Ask for It: LLMs Show Deficiencies in Active Multi-Turn Information Acquisition for Abductive Inference

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Large language models are worse at abductive inference when they must actively gather evidence over multiple turns, forming hypotheses that fit self-selected evidence without discriminating among alternatives.

desk verdict A genuinely useful interactive benchmark for active evidence acquisition, but the headline overfitting claim rests on unvalidated self-reports and an overstatement of what 'unclaimed wins' mean. read the letter →

arxiv 2608.03388 v1 pith:53CYMBYM submitted 2026-08-04 cs.CL

Shahrukh Mohiuddin , Chalamalasetti Kranti , Sherzod Hakimov , David Schlangen This is my paper · ORCID
classification cs.CL
keywords abductivereasoningactiveevidenceacquisitionmulti-turninteractionblack-boxfunctioninductionhypothesisretrodictionaccuracyLLMagentsstoppingbehaviourqueryselection
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that standard single-turn evaluations miss a central weakness of LLM reasoning: when a model must acquire evidence itself over multiple turns, revise its hypotheses, and decide when to stop, it performs substantially worse than when the same evidence is supplied. The authors build the Alien Abduction game, in which a model must reconstruct a hidden Python function from its signature and a limited number of black-box queries, with six modes that vary who selects the evidence and whether feedback is exact outputs or true/false verdicts. Across four models, success is highest when evidence is shown upfront, intermediate when examples are fed passively, and lowest when the model chooses its own probes. Models that pick their own evidence end up with hypotheses that reproduce that narrow evidence well, but those hypotheses do not distinguish the true function from rivals; some models submit too early, while others exhaust the budget while holding a correct but unsubmitted hypothesis. If correct, the findings imply that active evidence management, query selection, hypothesis validation, and stopping, is a distinct bottleneck in agentic LLM reasoning.

What carries the argument

The load-bearing instrument is the Alien Abduction game, a black-box protocol in which a Game Master hides a Python function and the model either proposes test inputs or receives examples, with feedback as exact outputs or as true/false membership verdicts; six modes factor control over evidence selection (active versus passive) against feedback form (output versus verdict), holding the same 50 target functions across modes. Two metrics carry the argument: Turn Budget Use (TBU), the fraction of the 15-turn budget consumed before submission, and Hypothesis Retrodiction Accuracy (HRA), the fraction of observed evidence reproduced by the model's self-reported current hypothesis. The factorization allows the active-passive comparison to isolate the effect of self-directed exploration, and HRA is what separates inferring the wrong rule from ignoring one's own evidence.

What would settle it

Score the model's last reported hypothesis (or force it to submit that hypothesis) in every episode instead of the submitted answer. If the model with a 53.6% unclaimed-win rate turns most of those failures into wins and the active-passive success gap narrows sharply, the deficiency is in commitment and format rather than abductive evidence management; if the gap persists, the claim survives.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is the active-information-acquisition deficit: in the Alien Abduction game, providing evidence upfront leads to higher success rates than distributing it across turns, and across models the single-turn modes outperform the passive multi-turn modes, which in turn outperform the active modes. When models select their own queries, they achieve lower success than when examples are provided by the oracle, yet their final reported hypotheses are more consistent with the evidence they selected, as measured by Hypothesis Retrodiction Accuracy. The authors interpret this as evidence that models form hypotheses that fit self-selected evidence without sufficiently distinguishing them from alternatives, and that they struggle to validate and refine hypotheses or to determine when to stop. Supporting observations include early commitment before the available evidence is used, exhaustion of the turn budget without convergence, narrow input coverage of self-selected queries, and unclaimed wins in which a correct hypothesis is reached but never submitted.

Load-bearing premise

The load-bearing premise is that the hypotheses a model reports in each turn accurately reflect what it actually believes; the paper itself concedes these reports may not represent the model's internal belief state, so the consistency and stopping findings would weaken if self-reports are noisy or decoupled from operative reasoning.

Editorial extensions

If this is right

  • Single-turn benchmarks overstate agentic competence: high performance on upfront-evidence tasks will not transfer to settings where an LLM agent must gather evidence, so interactive evaluations must include querying and stopping.
  • Deployments that let an LLM choose its own data points, such as tool use, data collection, or experiment design, should expect lower success than pipelines that feed curated examples, unless query selection is explicitly improved.
  • A hypothesis that fits all evidence the model itself collected is a weak success signal; agents need to seek evidence that discriminates among live alternatives, not just evidence consistent with the current guess.
  • Stopping is a real failure mode: models either commit too early or keep querying while holding a correct hypothesis, so explicit stopping criteria are as important as better induction.
  • Membership-verdict feedback is harder than exact-output feedback, especially when the model must propose the candidate pairs, meaning binary feedback regimes demand extra support.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct test of the overfitting explanation: if models are forced to include probes chosen by a broad random sampler or to state which live alternative each query discriminates, the active-passive gap should shrink; otherwise the gap is a query-selection problem, not a validation problem.
  • The behavioral pattern resembles confirmation bias in human hypothesis testing: models appear to prefer hypothesis-consistent probes over falsifying ones. Building a version of the game that counts whether queries target surviving alternatives would connect LLM active learning to that literature.
  • The unclaimed-win episodes suggest that for some models the binding constraint is commitment, not inference: prompting the model to submit whenever its reported hypothesis reproduces all evidence (HRA of 1) is a cheap intervention that would test whether success rises without any improvement in induction.
  • Parser errors were frequent for two of the four models, so part of the active-mode deficit may be protocol adherence rather than reasoning; comparing a free-form or tool-calling interface against the rigid format would separate the two.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper introduces Alien Abduction, a multi-turn benchmark game for studying how LLMs acquire evidence, update hypotheses, and decide when to stop in abductive inference. A model must infer a hidden Python function from only its signature and a limited interaction budget, with six modes varying who controls evidence selection (active vs. passive) and the feedback format (exact outputs vs. binary verdicts), plus two single-turn baselines. The benchmark uses 50 automatically generated and validated target functions across five domains, with the same targets used across modes, sandboxed execution of submitted solutions, and held-out test evaluation. Across four models, the authors report success rates, turn-budget use, hypothesis retrodiction accuracy (HRA), and qualitative trace analyses. The main empirical findings are that providing evidence upfront or passively leads to higher success than active multi-turn acquisition; some models commit before using the available evidence while others exhaust the budget without converging; final hypotheses in active modes are more consistent with self-selected evidence; and some failures are 'unclaimed wins' where a correct hypothesis is apparently reached but never submitted.

Significance. If the behavioral interpretation holds, the paper fills a genuine gap: it jointly varies evidence control and feedback form while scoring intermediate hypotheses rather than only final answers, and it does so with a controlled, reproducible protocol. The evaluation is machine-checked in the important parts: every submitted solution is executed in a sandbox against held-out tests, targets are automatically validated, a fixed seed governs target sampling, and the same 50 targets are used across all modes and models. The authors also provide an unusually honest Limitations section, explicitly flagging the GPT5.4-as-generator distributional bias and the self-reported nature of HRA. Even if the interpretive claims about overfitting and stopping need tightening, the benchmark itself and the descriptive success-rate and turn-budget results are a useful contribution to the study of interactive LLM reasoning.

major comments (3)
  1. [Section 4.3, Section 5.3, Figure 5] The headline contrast—final-turn HRA of 0.85–1.00 for successful active episodes versus 0.20–0.55 elsewhere—is computed from the model's self-reported 'current hypothesis' field. The manuscript does not state whether the reported hypothesis code is executed against the observed evidence, nor how non-executable reports (parser-error rates of 40.6% for Qwen3.6-35B and 45.2% for Mistral-Large-3, Table 5) or 'unknown' hypotheses are scored in HRA. Because the abstract's overfitting claim ('hypotheses that fit self-selected evidence without sufficiently distinguishing them from alternatives') rests on this contrast, the authors should either report HRA computed by executing the reported hypotheses on the observed evidence, or explicitly restrict the claim to 'reported' hypotheses and add a sensitivity analysis excluding parser-error turns.
  2. [Section 6, Table 4] The unclaimed-win analysis calls an episode an unclaimed win when the model reaches a correct hypothesis but never submits it. As with HRA, 'correct hypothesis' is apparently determined from the self-reported field, not from executing the reported code. For GPT5.4-mini, 53.6% of failed instances are classified this way; if the report is a post-hoc paraphrase that happens to match the observed examples, the figure overstates the claim that the model 'does not reliably assess when its hypotheses are sufficiently supported.' The authors should validate these last-turn reports by executing them against the held-out test suite (or at least against all evidence observed in the episode) and report the agreement rate.
  3. [Section 5.2, Figure 2] The ordering claim that success rates follow single-turn > passive > active is presented without significance testing. With 50 targets per mode-model cell, several mode contrasts appear within overlapping 95% confidence intervals in Figure 2, so the descriptive ordering alone does not establish the general conclusion. A paired or bootstrap test across the fixed 50 targets, or reported confidence intervals for the differences between modes, would strengthen the central comparative claim without changing the experimental design.
minor comments (4)
  1. [Section 4.3] The TBU formula appears as 'TBU = (n-1 T)' in the text; it should be '(n-1)/T'.
  2. [Section 6] The sentence '7.56% of failed instances of GPT5.4-mini's end with a correct hypothesis' contains a possessive typo; it should read 'of GPT5.4-mini end'.
  3. [Table 4] The model name 'GPT5.4-Mini' is capitalized inconsistently with 'GPT5.4-mini' used throughout the rest of the paper.
  4. [Section 4.4] The statement that source code and test instances 'will be released upon the paper's acceptance' limits reproducibility during review; consider providing an anonymized artifact link or a detailed protocol appendix.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an empirical benchmark whose headline contrasts are measured variables, not quantities fitted or defined into existence.

full rationale

This paper reports an empirical benchmark study rather than a formal derivation, so the main circularity patterns do not apply. The central quantities (success rate, TBU, HRA) are measured from logged interaction traces and executed submissions, and the headline contrast (higher success under oracle-provided evidence; higher final-turn HRA in active modes) is an empirical correlation between independently measured variables. The HRA metric is defined as retrodiction of observed evidence by the self-reported hypothesis; the claim that high HRA in active modes reflects overfitting to self-selected evidence is an interpretation of that measurement and is explicitly hedged in Section 5.3 ('may therefore reflect consistency with its current hypothesis rather than the ability of those examples to distinguish it from competing hypotheses') and in Limitations item 4 ('may not fully represent the model's internal belief state'). These are acknowledged measurement-validity caveats, not circular definitions. The use of GPT5.4 to generate the target pool and also evaluate GPT5.4 (Section 4.1) is a self-referential design choice that could bias model comparisons, but it does not make any reported quantity equal to an input by construction; the authors acknowledge the distributional-bias risk in Limitations item 2. The clembench citation (Chalamalasetti et al., 2023) is an infrastructure self-citation and is not load-bearing for the empirical findings. The unclaimed-wins analysis (Section 6, Table 4) depends on the self-reported 'current hypothesis' field and is not fully specified, but the paper treats that field as observable behavior rather than equating it with the model's operative belief by construction. No equation in the paper reduces a claimed result to its own input, so no circular step is exhibited.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

This is an empirical benchmark study, not a derivation. There are no invented physical entities and no fitted constants. The free parameters listed are hand-chosen design settings that directly shape the headline metrics (TBU threshold, single-turn baselines, success criterion). The axioms are the unstated or partially stated premises on which the observational claims rest; the authors explicitly flag axioms 3 and 4 in the Limitations.

free parameters (3)
  • Turn budget T=15 = 15
    Chosen in preliminary experiments to avoid truncation or aborted runs (Appendix B). It defines what counts as 'exhausting the budget' and shapes the TBU and success results across all modes.
  • Single-turn evidence batch size = 10 examples
    Fixed batch of ten evidence items shown upfront in STO and STV (Section 4.4). Single-turn success rates, used as the baselines for the multi-turn comparison, depend on this size.
  • Held-out test cases per target = 100
    Each target is paired with 100 type-aware test cases that define the success check (Section 4.1). The discriminative power of this set determines whether 'passes all tests' approximates functional equivalence.
assumptions (4)
  • domain assumption Single-run API evaluation under default decoding is representative of each model's behavior.
    Each model plays each target once per mode with default decoding parameters (Section 4.4). No seeds, temperatures, or repeated runs are reported, so sampling noise is not quantified beyond binomial confidence intervals.
  • domain assumption Passing 100 held-out test cases is treated as recovering the hidden function.
    Success is defined as agreement on the held-out suite, not proven equivalence (Sections 3.1 and 4.1). The edge-case pools make this plausible for primitive functions, but a non-equivalent function agreeing on all 100 cases would be scored as a win.
  • domain assumption Self-reported hypotheses measure the model's operative hypothesis.
    HRA is computed from hypotheses the model reports each turn (Section 4.3). The paper flags that these 'may not fully represent the model's internal belief state' (Limitations item 4); the stopping and consistency claims rest on this premise.
  • domain assumption The GPT5.4-generated target pool does not advantage GPT5.4 in the model comparison.
    Targets are generated by GPT5.4, validated, and randomly sampled (Section 4.1). The same targets face all models, so mode comparisons are controlled, but model rankings may be biased; the authors acknowledge this residual risk (Limitations item 2).

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Cite this review

Pith. "Pith review of Don't Let Me Ask for It: LLMs Show Deficiencies in Active Multi-Turn Information Acquisition for Abductive Inference." pith.science (2026). https://pith.science/paper/53CYMBYM

@misc{pith2026260803388,
  author       = {Pith},
  title        = {Pith review of: Don't Let Me Ask for It: LLMs Show Deficiencies in Active Multi-Turn Information Acquisition for Abductive Inference},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/53CYMBYM}},
  note         = {Machine review of arXiv:2608.03388}
}
read the original abstract

Abductive reasoning requires forming hypotheses that explain observed evidence and revising them as new evidence becomes available. While large language models (LLMs) are often evaluated on whether they solve abductive reasoning tasks correctly, less is known about how they acquire evidence, update their hypotheses, and decide when to stop. We introduce Alien Abduction game, an interactive probe for studying these behaviours under different interaction modes. The modes vary in whether evidence is provided upfront or across turns, and whether queries are selected by the model or examples are provided by the oracle. Across models, providing evidence upfront leads to higher success rates than distributing it across turns. In multi-turn settings, some models commit before using the available evidence, while others exhaust the turn budget without converging. Models also achieve higher success rates when examples are provided by the oracle than when they select their own queries, although their final hypotheses are more consistent with the evidence they selected. These findings suggest that models may form hypotheses that fit self-selected evidence without sufficiently distinguishing them from alternatives, and may struggle to validate and refine their hypotheses or determine when to stop.

Figures

Figures reproduced from arXiv: 2608.03388 by the authors.

Figure 1
Figure 1. Overview of Alien Abduction. The Game Master hides a target Python function f and interacts with the LLM through a black-box protocol. In the Active-Output mode shown here, the model proposes test inputs, receives the corresponding outputs, and even￾tually submits a final hypothesis as Python code. The submitted hypothesis is then evaluated after submission against the hidden target function on held-out test cases. … view at source ↗
Figure 2
Figure 2. Success rates across interaction modes for each model, with 95% confidence intervals. Dashed horizontal [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. TBU scores across models and interaction modes. Higher scores indicate more of the turn budget was [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (23 more)
Figure 4
Figure 4. Figure 4: Hypothesis retrodiction accuracy across turns. Dotted lines show the median turn count for each model. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Hypothesis retrodiction accuracy at the final turn across models and interaction modes. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Negative evidence impact on task success for GPT5.4 in Numbers domain. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 8
Figure 8. Figure 8: Spread of input queries: These results are for GPT5.4 model, for the two_numbers category. The narrower coverage of Active-output queries may con￾tribute to the higher failure rate. self-selection can limit input coverage and reduce success in active-output. Together, …
Figure 9
Figure 9. Figure 9: Qualitative analysis of GPT-5.4 on all variants. The chosen actions and corresponding outputs are given [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]
Figure 10
Figure 10. Figure 10: GPT-5.4-mini outputs for six variants Qwen3.6 Active-Outputround:0 Prompt, func. sig., #Turns, [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]
Figure 11
Figure 11. Figure 11: Qwen-3.6 outputs for six variants 15 [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 12
Figure 12. Figure 12: Mistral outputs for six variants 16 [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]
Figure 13
Figure 13. Figure 13: Condensed initial prompts for the six game modes of Alien Abduction, shown for a Number domain [PITH_FULL_IMAGE:figures/full_fig_p017_13.png]
Figure 14
Figure 14. Figure 14: Task success rates across models and interaction modes for the Number and Number Pairs domains. [PITH_FULL_IMAGE:figures/full_fig_p017_14.png]
Figure 15
Figure 15. Figure 15: Task success rates across models and interaction modes for the String and List domains. Missing bars [PITH_FULL_IMAGE:figures/full_fig_p018_15.png]
Figure 16
Figure 16. Figure 16: Task success rates across models and interaction modes for the Logic domain. Missing bars indicate zero [PITH_FULL_IMAGE:figures/full_fig_p018_16.png]
Figure 17
Figure 17. Figure 17: Hypothesis Retrodiction Accuracy across models and interaction modes for the Number and Number [PITH_FULL_IMAGE:figures/full_fig_p019_17.png]
Figure 18
Figure 18. Figure 18: Hypothesis Retrodiction Accuracy across models and interaction modes for the String and List domains. [PITH_FULL_IMAGE:figures/full_fig_p019_18.png]
Figure 19
Figure 19. Figure 19: Hypothesis Retrodiction Accuracy across models and interaction modes for the Logic domain. Missing [PITH_FULL_IMAGE:figures/full_fig_p019_19.png]
Figure 20
Figure 20. Figure 20: Confidence scores at the final turn across models and interaction modes. [PITH_FULL_IMAGE:figures/full_fig_p019_20.png]
Figure 21
Figure 21. Figure 21: Negative evidence impact on task success across verdict modes for GPT5.4 in List domain. [PITH_FULL_IMAGE:figures/full_fig_p020_21.png]
Figure 22
Figure 22. Figure 22: Negative evidence impact on task success across verdict modes for GPT5.4 in Logic domain. [PITH_FULL_IMAGE:figures/full_fig_p020_22.png]
Figure 23
Figure 23. Figure 23: Negative evidence impact on task success across verdict modes for GPT5.4 in Number Pairs domain. [PITH_FULL_IMAGE:figures/full_fig_p020_23.png]
Figure 24
Figure 24. Figure 24: Distribution of input queries across interaction modes for GPT5.4 in the List domain. [PITH_FULL_IMAGE:figures/full_fig_p020_24.png]
Figure 25
Figure 25. Figure 25: Distribution of input queries across interaction modes for GPT5.4 in the Logic domain. [PITH_FULL_IMAGE:figures/full_fig_p021_25.png]
Figure 26
Figure 26. Figure 26: Distribution of input queries across interaction modes for GPT5.4 in the Numbers domain. [PITH_FULL_IMAGE:figures/full_fig_p021_26.png]
Figure 27
Figure 27. Figure 27: Interaction state at the last turn across models and interaction modes. [PITH_FULL_IMAGE:figures/full_fig_p021_27.png]

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Reference graph

Works this paper leans on

20 extracted references · 18 canonical work pages

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    ""Returns True if x is non-negative , otherwise False

    (10, 10) | 7. (1000, 1000) | 8. (2, 2) | 9. (-1, 1) | 10. (1, 1) 2 SOLVE: “‘python def solution(x): return x “‘ 3 Game Verdict: LOSS 4 Incorrect. Failed Cases: Input: [-10] | Expected: 10 | Got: -10 Input: [-2] | Expected: 2 | Got: -2 Input: [-99] | Expected: 99 | Got: -99 5 def absolute_value(x: int) -> int: return abs(x) Table 6: A lost Single-Turn-Outp...

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    ""Returns x unchanged if it is non-negative , otherwise returns x plus 7

    ((-2, 4), False) r:1 SOLVE: def f(x): """Returns x unchanged if it is non-negative , otherwise returns x plus 7""" return ( x if x >= 0 else x + 7 ) Evaluation: WIN GPT 5.4 Prompt, func. sig., #Turns, Output & Solve formatting and valid example Figure 9: Qualitative analysis of GPT-5.4 on all variants. The chosen actions and corresponding outputs are give...

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    ""Returns x unchanged i f nonnegative , otherwise adds 7

    (-2, 5) r:1 SOLVE: def f(x): """Returns x unchanged i f nonnegative , otherwise adds 7""" return ( x if x >= 0 else x + 7 ) Evaluation: WIN Passive-Output round:0r:1 Invalid InputOUTPUT:Parse Error r:2 NEXTOUTPUT: (1000, 1000) r:3 NEXTOUTPUT: (-99, -92) r:6 NEXTOUTPUT: (99, 99) r:7 SOLVE: def f(x): """Returns x unchanged if it is nonnegative; otherwise re...

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    ""Returns x unchange d if non-negative , otherwise adds 7

    (-2, 5) r:1 SOLVE: def f(x): """Returns x unchange d if non-negative , otherwise adds 7.""" return ( x + 7 if x < 0 else x ) Evaluation: WIN Passive-Output round:0r:1 Invalid InputOUTPUT:Parse Error r:2 NEXTOUTPUT: (1000, 1000) r:3 NEXTOUTPUT: (-99, -92) r:6 NEXTOUTPUT: (99, 99) r:9 SOLVE: def f(x): """ A dds 7 to negative integers, otherwise returns x un...

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    ""Returns True when the two integers have the same remainder modulo 7

    ((-2, 4), False) SOLVE: def f(x): """Returns True when the two integers have the same remainder modulo 7.""" a, b = x return ( a % 7 == b % 7 ) r:1 Evaluation: WIN GPT 5.4m Prompt, func. sig., #Turns, Output & Solve formatting and valid example Figure 10: GPT-5.4-mini outputs for six variants Qwen3.6 Active-Output round:0 Prompt, func. sig., #Turns, Outpu...

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    sig., #Turns, Output & Solve formatting and Preloaded examples:

    (-2, 5) Invalid Input r:1 Evaluation: LOSS Passive-Output round:0r:1 Invalid InputOUTPUT:Parse Error r:2 NEXTOUTPUT: (1000, 1000) r:3 NEXTOUTPUT: (-99, -92) r:6 NEXTOUTPUT: (99, 99) r:15 NEXTEvaluation:LOSS(Turn limit) Qwen3.6 Single-Turn-Verdict round:0 Prompt, func. sig., #Turns, Output & Solve formatting and Preloaded examples:

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    ((-2, 4), False) Invalid Input r:1 Evaluation: LOSS Qwen3.6 Prompt, func. sig., #Turns, Output & Solve formatting and valid example Figure 11: Qwen-3.6 outputs for six variants 15 Mistral Active-Output round:0 Prompt, func. sig., #Turns, Output & Solve formatting and valid exa...

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    ((-2, 4), F alse) Invalid Input r:1 Evaluation: LOSS Mistral Active-Verdict round:0 Prompt, func. sig., #Turns, Output & Solve formatting and valid example r:1 Invalid InputOUTPUT:Parse Error r:2 INPUT: 0OUTPUT: True r:3 INPUT: 1OUTPUT: True r:6 Invalid InputOUTPUT: Parse Erro...

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    (’a-b’, ’a’) | 7. (’ ’, ’ ’) | 8. (’aba’, ’a’) | 9. (’ ’, ’ ’) | 10. (”, ”) 2 SOLVE: “‘python def solution(text: str) -> str: return text[0] if text else ” “‘ 3 Game Verdict: WIN 4 That is correct! 5 def first_char(text: str) -> str: return text[:1] Table 7: A won Single-Turn-...

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Reviewed August 15, 2026 · model on record in the stance chip above.