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REVIEW 3 major objections 5 minor 58 references

From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read AR-Bench tests whether LLMs can gather their own missing information; the paper presents evidence they cannot, with GPT-4o reaching only 35% on the guessing-number task and process scores plateauing near half of key questions.

desk verdict Solid benchmark with a likely-true headline finding; the LLM-judge generalization and missing error bars are the main caveats. read the letter →

arxiv 2506.08295 v1 pith:PLZXNFTJ submitted 2025-06-09 cs.LG cs.AIcs.CL

Zhanke Zhou , Xiao Feng , Zhaocheng Zhu , Jiangchao Yao , Sanmi Koyejo , Bo Han This is my paper
classification cs.LGcs.AIcs.CL
keywords activereasoningquestionaskingincompleteinformationbenchmarklargelanguagemodelsseekingmulti-turninteractionevaluation
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 introduces AR-Bench, a benchmark that asks whether large language models can solve problems when the information needed is not handed to them up front. Its central claim is that state-of-the-art models are far weaker at this 'active reasoning' than at the passive reasoning that existing benchmarks measure: across 25 rounds of interaction, GPT-4o identifies the correct answer in only 54% of detective cases, 63% of situation puzzles, and 35% of guessing-number games, while human participants reach 80%, 67%, and 100%. The paper argues that the bottleneck is asking useful questions rather than drawing conclusions, since process scores plateau at roughly half the key questions resolved, and additional rounds, tree search, and fine-tuning all yield little gain. A sympathetic reading takes the paper to establish that asking the right question under incomplete information is a distinct capability that current LLMs have not acquired.

What carries the argument

The load-bearing object is AR-Bench itself: three task families — detective cases, where the model interrogates five suspects to find a murderer (commonsense reasoning); situation puzzles, where it asks a judge yes-or-no questions to reconstruct the truth of a lateral-thinking riddle (logical reasoning); and guessing numbers, where it cracks a four-digit secret with digit-position feedback from a rule-based oracle (symbolic reasoning). Each puzzle is played as a 25-round conversation between the model under test and judge agents that hold the ground truth. Two metrics separate the failure modes: the outcome score measures the final answer (accuracy, F1, or exact match), while the process score measures, at each conversation state, how many of the puzzle's key questions have been resolved — with a Llama-3.1-405B verifier judging DC and SP states and an exact digit-count formula for GN. The process metric is what carries the claim that information acquisition, not deduction, is the limiting step.

What would settle it

Give a failing model the complete answer key up front (every key question answered truthfully) and ask it to produce the final solution: if it then scores near ceiling, the question-asking bottleneck is confirmed, while if it still fails, deduction under full information is the weak spot. Conversely, replace the Llama-3.1-405B judge with human annotators on a sample of detective-case and situation-puzzle runs and compare outcome and process scores: if they move materially, part of the reported gap is a judge artifact rather than a model deficiency.

Watch

Extended reading notes

Core claim

The paper's central discovery is that under incomplete information, contemporary LLMs fail in the question-asking phase of reasoning rather than the deduction phase. On AR-Bench, process scores — the fraction of puzzle-defining key questions whose answers the model's conversation has uncovered — rise quickly in the first few rounds and then flatten, with GPT-4o reaching roughly 51% in detective cases and 44% in situation puzzles after 25 rounds. Methods that succeed in passive reasoning transfer poorly: tree-of-thought matches or regresses on detective cases and situation puzzles, supervised fine-tuning scores 0% on guessing numbers, direct preference optimization underperforms zero-shot on two of three tasks, and the two dedicated active-reasoning methods, Proactive CoT and Uncertainty-of-Thought, give marginal or negative results. The paper concludes that the divergence between passive and active reasoning is stark and that improving active reasoning requires training with interactive learning, real-time feedback loops, and environment-aware objectives.

Load-bearing premise

The whole evaluation rests on the assumption that the non-player judges — chiefly Llama-3.1-405B for detective cases and situation puzzles — answer every model's questions correctly and consistently, with the 96% reliability measured on 200 GPT-4o situation-puzzle questions assumed to hold for other tasks and other models, and that each puzzle's ground truth is uniquely correct as keyed.

Editorial extensions

If this is right

  • Models that dominate passive-reasoning benchmarks will still fail agentic applications that require gathering missing information, so information-seeking ability must be part of any serious agent evaluation.
  • Allowing more interaction rounds is not a cure: the process-score gain falls from an average of 7.7% between rounds 5 and 10 to 2.5% between rounds 20 and 25, and scaling to 100 rounds leaves DC and SP outcomes roughly unchanged.
  • Search and post-training remedies as currently practiced do not close the gap, since ToT, SFT, DPO, Proactive CoT, and UoT all deliver little or no benefit, implying new objectives that reward information gain are needed.
  • Question-asking quality scales with model size — Llama-3.1-405B and GPT-4o lead on process scores — so progress on active reasoning is partly a scaling story, but even the largest model resolves only about half of the key questions in 25 rounds.
  • The process metric gives a concrete training target: a method that raises key-question resolution toward 100% should pull outcome scores up with it, making AR-Bench a testbed for interactive-learning algorithms.

Reading between the lines

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

  • The paper's dichotomy suggests a reusable principle for benchmark design: a reasoning benchmark should first verify that the model can acquire the information the reasoning needs, because a model that fails to ask will look equally weak at deduction regardless of its actual deductive ability.
  • In the guessing-number task the answer space is fully enumerable (5,040 numbers), so the key-question metric could be sharpened into an exact expected-information-gain score per guess; the paper's plateau finding predicts that current models' guesses deliver far less than the information-theoretic optimum.
  • The judge-reliability check covers only GPT-4o's situation-puzzle questions; a natural extension is to re-run detective cases and all other models with independently verified answers, or with a different judge model, to test whether the reported model ordering is stable.
  • If the bottleneck is genuinely question generation, then collecting human question-asking traces — as the paper itself suggests in its future-directions appendix — should transfer more directly to AR-Bench performance than the outcome-only DPO data used here.
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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 / 5 minor

Summary. The paper introduces AR-Bench, a benchmark for evaluating active reasoning in LLMs under incomplete information. It contains three task families: detective cases (DC), situation puzzles (SP), and guessing numbers (GN), spanning commonsense, logical, and symbolic reasoning. The evaluation protocol has the LLM under test play a multi-round questioning game against an NPC implemented by Llama-3.1-405B (for DC and SP) or a rule-based oracle (for GN), after which the model gives a final answer. Outcome metrics (accuracy, F1, exact match) and a process metric based on key questions are reported for eight LLMs and several prompting/training methods. The central finding is that current LLMs, including GPT-4o, achieve low outcome scores (e.g., 35% exact match in GN) and process scores that plateau around half of the key information, suggesting that the bottleneck in active reasoning is question asking rather than final deduction. The authors also report ablations showing limited gains from tree-of-thought, SFT, DPO, and other methods, and provide a public benchmark repository.

Significance. If the central claim holds, AR-Bench fills a real gap in LLM evaluation: existing benchmarks mostly measure passive reasoning with complete information, while many real-world applications require iterative questioning. The paper's strengths include a large-scale generated dataset with human verification of ground truths, a judge-reliability audit on 200 SP questions, a publicly released benchmark, and a systematic comparison of models and methods. The finding that modern LLMs fail to actively acquire the information needed to solve problems is important and falsifiable. However, the headline conclusion rests on an LLM judge whose reliability is only partially measured, and the evaluation lacks error bars or multiple seeds. These issues are fixable and do not invalidate the benchmark's usefulness, but they need to be addressed before the quantitative claims can be fully accepted.

major comments (3)
  1. [Sec. 4.12, Appendix A.2, Listing 6] The judge reliability audit is load-bearing but incomplete. The only reliability check reported is based on 200 questions generated by GPT-4o for the SP task, where Llama-3.1-405B achieves 96% accuracy. This audit does not cover DC, where NPCs are instructed to role-play with potentially deceptive behavior (Listing 6 explicitly tells one suspect to 'sow confusion'), nor does it stratify by the model asking the questions. Because Observations 4.1, 4.5, and 4.8 are computed against answers from this same judge, the cross-task and cross-model generalization of the judge is unverified. The authors should extend the audit to DC questions, include questions from all evaluated models, and report agreement per task and per asking model.
  2. [Sec. 3, Eq. (1)] The process score f(st, qi) is implemented by prompting Llama-3.1-405B to decide whether the current state resolves a key question, but no human agreement or inter-annotator analysis is reported for these labels. The process-score plateau that underlies Observations 4.5 and 4.8, as well as the positive/negative labels used for SFT and DPO in Sec. 4.1, all depend on this unvalidated oracle. The authors should validate f on a held-out sample with human labels and test sensitivity by comparing with an alternative judge, otherwise the claim that question quality is the primary bottleneck is underdetermined.
  3. [Sec. 4.1, Tabs. 5-6] No error bars, seeds, or significance tests are reported. Many comparisons are small in magnitude (e.g., Llama-3.1-8B DC zero-shot 31 vs. few-shot 32; Qwen-2.5-3B GN 0 vs. Qwen-2.5-7B GN 4), and GN scores of 0 vs. 1 are within a single sample. Since AR-Bench is intended as a benchmark for comparing methods and models, the authors should report means and variances over multiple runs, ideally with statistical tests or a stated threshold for meaningful differences.
minor comments (5)
  1. [Observation 4.2] The opening sentence says few-shot and zero-shot instruction methods outperform standard zero-shot, but Fig. 4(a) shows few-shot instruction (27) below zero-shot (31) for Llama-3.1-8B in DC; the wording should be adjusted to describe the actual cross-task pattern.
  2. [Sec. 3, GN score formula] The GN process score formula states a digit is counted as misplaced if it appears in g but not at position i, with the caveat 'to avoid double-counting, each digit in g is considered only once.' As written, the formula can double-count if the guess contains duplicate digits; please specify that guesses are restricted to unique digits or provide the precise matching algorithm.
  3. [Table 3 / Table 18] Within a task, error-pattern proportions can sum to more than 100% (e.g., SP Llama-3.1-8B: 36% + 90% = 126%). If each error case can exhibit multiple error types, state this explicitly so the table is not misread as exclusive categories.
  4. [Listing 26] There is a typo: '25 quesions' should be '25 questions', and the output-format instruction is repeated; please clean up the prompt text.
  5. [Appendix D.2, SFT analysis] The explanation that SFT 'tends to memorize training data rather than foster active reasoning skills' is presented as a finding but is not directly supported by the reported experiments; please phrase it as a hypothesis or add supporting evidence such as a training-dynamics analysis.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claims are empirical evaluations against fixed ground truths, not derivations from the benchmark's own inputs.

full rationale

AR-Bench's load-bearing claim—that current LLMs underperform in active reasoning—rests on outcome metrics (accuracy, F1, exact match) computed against fixed ground truths, plus a process score defined by an explicit formula Score(Q, st) = (1/|Q|) sum_i I[f(st, qi)=1]. The key questions Q are extracted during dataset construction from the generation tree, before any evaluated model is run, so the process metric is an operational definition of information acquisition rather than a quantity derived from the model being evaluated. The use of Llama-3.1-405B as the NPC answerer and as the implementation of f is a measurement-validity concern (the reliability check in Sec. 4.12 covers only 200 GPT-4o SP questions), but it is not circular: no parameter is fitted from the evaluated outputs, and no prediction reduces by construction to an input. The paper's self-citations (e.g., Zhou et al. on robustness and reasoning traces) appear only in related work and are not load-bearing for the empirical findings. No uniqueness theorem, ansatz, or renamed known result is used to force the conclusions. The central result is therefore self-contained with respect to its own definitions.

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

The central evaluation rests on the reliability of an LLM judge and the solvability of synthetic puzzles; these are domain assumptions rather than mathematical axioms. There are no fitted free parameters. No new physical or theoretical entities are introduced.

assumptions (4)
  • domain assumption Llama-3.1-405B judge provides accurate answers to arbitrary player questions in DC and SP.
    Setup in Sec. 4.1 uses Llama-3.1-405B for interactive feedback in SP and DC; reliability is verified only on 200 SP questions generated by GPT-4o (Sec. 4.12, Fig. 12), not on DC or on questions from other models.
  • domain assumption The AR-Bench puzzles are solvable, with unique ground truths (DC: one murderer; SP: internally consistent explanation; GN: correct 4-digit number).
    Claims in Sec. C.4 that human intervention verified these properties; no independent audit is provided.
  • domain assumption The process score f, implemented by Llama-3.1-405B, correctly maps conversation state to resolved key questions.
    Defined in Sec. 3; the LLM-as-judge reliability is checked only on a small sample of SP questions, not on the full set of key questions for DC.
  • domain assumption The human evaluation with undergraduate students is representative of human performance.
    Sec. 4.2 (Fig. 7) and Tab. 16 report demo-level human evaluation; sample size and demographics are not reported.

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Cite this review

Pith. "Pith review of From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?." pith.science (2026). https://pith.science/paper/PLZXNFTJ

@misc{pith2026250608295,
  author       = {Pith},
  title        = {Pith review of: From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PLZXNFTJ}},
  note         = {Machine review of arXiv:2506.08295}
}
read the original abstract

While existing benchmarks probe the reasoning abilities of large language models (LLMs) across diverse domains, they predominantly assess passive reasoning, providing models with all the information needed to reach a solution. By contrast, active reasoning-where an LLM must interact with external systems to acquire missing evidence or data-has received little systematic attention. To address this shortfall, we present AR-Bench, a novel benchmark designed explicitly to evaluate an LLM's active reasoning skills. AR-Bench comprises three task families-detective cases, situation puzzles, and guessing numbers-that together simulate real-world, agentic scenarios and measure performance across commonsense, logical, and symbolic reasoning challenges. Empirical evaluation on AR-Bench demonstrates that contemporary LLMs exhibit pronounced difficulties with active reasoning: they frequently fail to acquire or leverage the information needed to solve tasks. This gap highlights a stark divergence between their passive and active reasoning abilities. Moreover, ablation studies indicate that even advanced strategies, such as tree-based searching or post-training approaches, yield only modest gains and fall short of the levels required for real-world deployment. Collectively, these findings highlight the critical need to advance methodology for active reasoning, e.g., incorporating interactive learning, real-time feedback loops, and environment-aware objectives for training. The benchmark is publicly available at: https://github.com/tmlr-group/AR-Bench.

Figures

Figures reproduced from arXiv: 2506.08295 by the authors.

Figure 1
Figure 1. In passive reasoning (a), the model is provided [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. PR derives the correct solution step by step, as seen in prompting methods like chain-of-thought and tree-of-thought. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Representative examples from AR-Bench, demonstrating distinct problem-solving tasks. We show the given [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: The evaluation results of outcome scores for Llama-3.1-8B and Llama-3.1-70B on the AR-Bench across various [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Reasoning accuracy on the AR-Bench with differ [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 8
Figure 8. Figure 8: The process score across three tasks, evaluating Llama-3.1-8B (a) and Llama-3.1-70B (b) with different methods. [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: The process score of different models across three tasks in AR-Bench. All models are in a zero-shot setting. [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: We present the results of scaling up the interaction rounds from 25 to 100 across three tasks using the Llama-3.1- [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]
Figure 11
Figure 11. Figure 11: The outcome scores of reasoning given the generated question-answering traces. We employ various models [PITH_FULL_IMAGE:figures/full_fig_p008_11.png]
Figure 12
Figure 12. Figure 12: Verifying the reliability (accuracy in judgement) of the LLM judge on the SP task. [PITH_FULL_IMAGE:figures/full_fig_p008_12.png]
Figure 13
Figure 13. Figure 13: Case studies of the three tasks in AR-Bench (with Zero-shot GPT-4o). [PITH_FULL_IMAGE:figures/full_fig_p009_13.png]
Figure 14
Figure 14. Figure 14: A generation example of the SP puzzles. The true murderer possesses all three attributes, while other suspects lack one attribute to establish their innocence. We also include a "blank" suspect with no connection to the crime, serving as a control character in the inv…

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Reference graph

Works this paper leans on

58 extracted references · 58 canonical work pages

  1. [1]

    Each testimony from the outline must follow via logical deduction from other suspects’ existing action

    work page

  2. [2]

    The testimony should simply explain like: xxx saw yyy entering the study room, do not give irrelevant information

    work page

  3. [3]

    The testimony you generate must match the structure of the outline I give you

    work page

  4. [4]

    The testimony must match the evidence of each suspect to provide more useful information to the detective

    work page

  5. [5]

    You should check the validation of the new testimony that does not conflict with existing knowledge

    work page

  6. [6]

    You should check the validation of the timeline that does not conflict with existing knowledge if given

    work page

  7. [8]

    your output should be also a json form. ## Example <example> ## Now create testimony of each suspect in following outline: Listing 14: Timeline Prompt Generation in DC Your task is to generate the timeline of each suspect in a murder mystery, as shown in the example. In this story, the timeline should match the case scenario and the information of the sus...

    work page

  8. [9]

    **Double−Check for Ambiguity**: Review the sentence to ensure that it doesn’t have multiple interpretations that could confuse the solver

    work page

Show all 58 references
  1. [10]

    the timeline must match the existing information of the outline I give you, including suspect’s reason_at_scene, suspicion, motive, opportunity, access_to_weapon, testimony

  2. [11]

    the timeline must be comprehensive that include the all time of the day, if given outline does not provide enough information to create the timeline, try to fullfill some irrelevant information

  3. [12]

    Your timeline should not conflict with previous generated timeline of other suspects

  4. [13]

    the key is_murderer is Yes), the timeline should involve the action about the murder (e.g

    If the suspect is the murderer (i.e. the key is_murderer is Yes), the timeline should involve the action about the murder (e.g. sneak to victim’s room and strike him with a knife)

  5. [14]

    the timeline should match the general information, such as the time, the location, the cause_of_death, the weapon and the initial_information

  6. [16]

    Your output should be a form of expansion of given outline, do not modify the original information about the outline

  7. [17]

    your output should be also a json form. ## Example: <example> ## Now create timeline of the given suspect in following outline: Listing 15: Suspect Story Generation Prompt in DC Create a story of the crime day of the suspect <suspect> You can mainly create the story based on t...

  8. [18]

    **Create a Paradoxical Scenario**: Craft a sentence that presents a situation that seems impossible or contradictory at first glance but has a logical explanation

  9. [19]

    **Keep it Concise**: The sentence should be brief and to the point, ideally no longer than one or two sentences

  10. [20]

    The goal is to pique curiosity, not to resolve it immediately

    **Avoid Spoilers**: Do not include the explanation or solution within the sentence. The goal is to pique curiosity, not to resolve it immediately

  11. [21]

    **Ensure Originality**: The scenario should be unique and not copied from existing puzzles or well−known paradoxes

  12. [22]

    **Maintain Clarity**: Use clear and unambiguous language to describe the scenario, avoiding overly complex vocabulary or convoluted sentence structures

  13. [23]

    **Encourage Logical Thinking**: The sentence should stimulate critical thinking and encourage solvers to ask probing questions to unravel the mystery

  14. [24]

    **Set a Realistic Context**: While the situation is counter−intuitive, it should be plausible within a real−world or logically consistent context

  15. [25]

    **Avoid Leading Language**: Do not include hints or clues that directly point to the solution within the sentence

  16. [27]

    **Check story type**: Ensure that the sentence aligns with the specified story type requirements

  17. [28]

    **Output Format**: Your output should adhere to the JSON format. ## Example: <example> ## Now create a new sentence following the story type: Story type: − Supernatural: <supernatural> − Someone dies: <lethal> Output: Listing 17: Question Generation Prompt in SP You are a good...

  18. [29]

    − **Someone Dies**: if the someone_died is yes,the death should be integral to the plot

    **Incorporate All Given Elements**: − **Supernatural**: If the ’supernatural’ element is included, ensure that the story involves aspects beyond the natural world, such as ghosts, magic, or unexplained phenomena. − **Someone Dies**: if the someone_died is yes,the death should ...

  19. [30]

    − The sequence of events should be clear and make sense within the story’s universe

    **Create a Cohesive and Logical Storyline**: − Even with supernatural elements, the story should have internal logic and consistency. − The sequence of events should be clear and make sense within the story’s universe

  20. [31]

    − Include subtle clues that lead to the solution, encouraging critical thinking

    **Develop a Compelling Mystery**: − Craft the story in a way that presents a puzzling situation or outcome. − Include subtle clues that lead to the solution, encouraging critical thinking

  21. [32]

    − Use vivid descriptions to create an immersive setting

    **Engage the Reader Emotionally**: − Develop well−rounded characters that the reader can connect with. − Use vivid descriptions to create an immersive setting

  22. [33]

    − Balance the amount of information given to avoid making the solution too obvious or too obscure

    **Maintain Suspense and Curiosity**: − Reveal information gradually to keep the reader intrigued. − Balance the amount of information given to avoid making the solution too obvious or too obscure

  23. [34]

    − Avoid stereotype associated with supernatural themes and character deaths

    **Ensure Originality**: − Create a unique storyline that hasn’t been overused in other situation puzzles. − Avoid stereotype associated with supernatural themes and character deaths

  24. [35]

    **Format Appropriately**: − just output the whole story in the key: bottom − Your output should adhere to the JSON format. ## Example <example> Now create a bottom based on the input: <story tree> Listing 20: Puzzle Generation Prompt in SP You are a good Situation Puzzle autho...

  25. [36]

    **Engaging Scenario**: Create a SURFACE that presents an intriguing and puzzling situation to capture the solver’s interest, even if the SURFACE looks strange

  26. [37]

    **Brevity and Clarity**: Keep the SURFACE concise and clear, avoiding unnecessary details that do not contribute to the puzzle

  27. [38]

    **Partial Clues**: the SURFACE only include partial clue, the situation puzzle encourages solver to ask more questions to reveal the BOTTOM

  28. [39]

    **Avoid Spoilers**: Do not reveal the BOTTOM directly or make the SURFACE too leading; maintain the mystery to challenge the solver

  29. [40]

    29 From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?

    **Originality**: Create an original scenario or put a unique twist on a familiar concept to make the puzzle stand out. 29 From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?

  30. [41]

    **Consistency**: The SURFACE should be consistent with the BOTTOM, make sure the BOTTOM can explain all details in the SURFACE

  31. [42]

    ## Example <example> Now create the Surface based on the input: <story tree> D

    **Format Appropriately**: Your output should adhere to the JSON format. ## Example <example> Now create the Surface based on the input: <story tree> D. Supplementary Results of the Evaluation D.1. Supplementary Settings Evaluation Scope. We evaluate the currently representativ...

    2024

  32. [43]

    yes" responses preferred over

    and Qwen-2.5 (3B, 7B) (Yang et al., 2025) as representatives of open-source non-reasoning LLMs. We also involve QwQ-32B - a powerful reasoning model. These models are widely used and compared in current leaderboards and arenas, which include both reasoning and non-reasoning mo...

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  33. [44]

    Fully understand the cause, process, and outcome of the entire story, and make logical inferences

  34. [45]

    If a conclusion cannot be drawn from the provided story or through reasonable inference, answer "Unknown"

  35. [46]

    Do not provide any additional explanations

    Strictly adhere to answering with only the three specified responses: Yes, No, or Unknown. Do not provide any additional explanations

  36. [47]

    This is not a question, please propose your next question

    Carefullty check whether the player ask for the answer, if a player do so, please only answer "This is not a question, please propose your next question." ## Examples <example> ## Question Content ### Surface <puzzle> ### Bottom <truth> Now, please judge the following player q...

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    How many digits are present in the answer and in the correct position

  38. [49]

    the manuscript,

    How many digits are present in the answer but in the different position from the guessing number For example: 0 digits are present in the answer and in the correct positions, 2 digits are present in the answer but in the different positions Game start: <guess record> Next gues...

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    Reed, but there was no evidence of her being present during the time of the murder

    Evelyn Carter had a complicated history with Dr. Reed, but there was no evidence of her being present during the time of the murder

  40. [51]

    Reed and left the library before the murder occurred

    Clara Whitmore had no conflicts with Dr. Reed and left the library before the murder occurred

  41. [52]

    Reed about a manuscript, and he was visibly agitated and pacing in the library

    Henry Collins had a heated discussion with Dr. Reed about a manuscript, and he was visibly agitated and pacing in the library. He also seemed to be waiting for something or someone, indicating possible involvement

  42. [53]

    Reed earlier, but he had an alibi for the time of the murder

    Michael Donovan had a tense conversation with Dr. Reed earlier, but he had an alibi for the time of the murder

  43. [54]

    Samantha Greene was present in the library but only overheard a conversation and did not seem to have a motive. Answer: C Ground Truth: A Listing 30: Complete case study of DC: Example 2 ## Intial Information Time: Evening Location: Old Manor House Victim: − Name: Jonathan Bla...

  44. [55]

    Evelyn Harper had a heated argument with Jonathan Blackwood about her research, and she admitted to being in his study around the time of the murder

    Dr. Evelyn Harper had a heated argument with Jonathan Blackwood about her research, and she admitted to being in his study around the time of the murder. She was seen leaving the manor in a hurry, which suggests she may have been involved in the incident

  45. [56]

    Marcus Ellison had an amicable relationship with Jonathan and has an alibi supported by security footage placing him at a different location from 6:30 PM onwards

  46. [57]

    He also reported seeing Dr

    Professor Alan Carter did not have any disagreements with Jonathan and was seen mingling with guests during the time of the murder. He also reported seeing Dr. Evelyn Harper entering and leaving the study

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    Laura Whitman was not present at the Old Manor House and has no direct involvement with Jonathan, eliminating her as a suspect

  48. [59]

    Evelyn Harper entered

    Sophia Bennett had a brief disagreement with Jonathan but left the study before Dr. Evelyn Harper entered. Her focus on unrelated matters and lack of motive make her less likely to be the murderer. Answer: A Ground Truth: A Listing 31: Complete Case Study of SP: Example 1 ## P...

    1985

  49. [2023]

    However, collecting pairwise preference data is resource-intensive

    fine-tunes LLMs using pairwise preference data, achieving significant performance gains when combined with Monte Carlo Tree Search (MCTS) (Zhang et al., 2024a; Xie et al., 2024). However, collecting pairwise preference data is resource-intensive. Recent advancements address th...

    2024

  50. [2024]

    At his own funeral, Mike stood among the mourners, unnoticed, as they grieved his death

    reduces computational overhead by eliminating the need for a reference model during training. On-policy Algorithms. On-policy algorithms optimize policies using data generated by the current policy under training. Techniques such as Proximal Policy Optimization (PPO) (Schulman...

    2024

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