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REVIEW 5 major objections 6 minor 92 references

Four-agent reasoning ensemble claims 49.92% on Humanity's Last Exam and lets a lightweight model outscore a heavier one.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · deepseek-v4-flash

2026-08-01 10:17 UTC pith:CO3FTT33

load-bearing objection Promising heterogeneous multi-agent framework, but the flagship HLE comparison is built on mismatched scoring protocols, so the headline 'heterogeneity beats scaling' claim is not yet trustworthy. the 5 major comments →

arxiv 2607.20268 v1 pith:CO3FTT33 submitted 2026-07-22 cs.AI cs.CL

PoTRE: Test-Time Reasoning inspired by Cognitive Heterogeneity

Anmol Kankariya , Sercan \"O. Ar{\i}k This is my paper
classification cs.AI cs.CL
keywords multi-agent reasoningheterogeneous ensemblestest-time computechain-of-thoughtHumanity's Last ExamARC-AGI-2Self-ConsistencyLLM synthesis
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper tries to establish that the way test-time reasoning is structured matters more than how much of it you do. It argues that common approaches replicate one reasoning style many times, so their errors stay correlated; PoTRE instead runs four structurally different reasoning agents in parallel—adversarial debate, hierarchical planning, breadth-first spectrum search, and direct chain-of-thought—and then reconciles their outputs with a task-adaptive synthesis layer. The central claim is that this heterogeneity yields better reasoning per token than homogeneous scaling, and that it can systematically let a smaller, cheaper model outperform a larger one on hard benchmarks. Reported results include 49.92% on Humanity's Last Exam, a near-doubling of baseline on ARC-AGI-2, and a new high score on PRBench Finance Hard.

Core claim

PoTRE's central discovery is that architecturally diverse reasoning paths fail differently, and that this 'error decoupling' converts into accuracy. On the paper's evidence, the four agents collectively generate the correct answer far more often than any single one (an oracle coverage of 64.6% on open-book HLE versus 58.4% recovered), and the synthesis layer rescues correct minority answers that a standard majority vote would discard, recovering 22–41% of outvoted correct candidates depending on the model. The paper also documents a boundary: when all four agents fully disagree, synthesis accuracy collapses toward chance, showing the method's gains depend on the synthesizer having comparativ

What carries the argument

The central mechanism is the four-agent ensemble plus a Task-Adaptive Aggregation Layer. The Adversarial Refinement Agent runs a proposer-verifier debate loop; the Hierarchical Strategic Planning Agent decomposes problems into sub-goals with an overseer that forces strategic pivots; the Spectrum Search Agent generates many parallel candidates and selects via judge or constraint-guided verification; the Direct Chain Agent provides a grounded sequential baseline. The synthesis layer then chooses among candidates for constrained tasks, merges rationales for open-ended tasks, or verifies rule-consistency for spatial tasks—deliberately avoiding naive majority voting.

Load-bearing premise

The comparison assumes the homogeneous self-consistency baselines on HLE are scored under the same semantic LLM-judge protocol as PoTRE; if they were scored by exact-string matching instead, part of the reported gap could be a grading artifact.

What would settle it

Re-score the self-consistency baselines on HLE with the same semantic judge used for PoTRE and check whether the margin persists; and run a temperature-diverse four-sample chain-of-thought ensemble at the same token budget as PoTRE to see whether architectural diversity outperforms plain stochastic diversity when costs are equal.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Heterogeneous reasoning structure can substitute for model scale: a lightweight model scaffolded this way is reported to beat a larger model on all three benchmarks.
  • Architectural diversity is claimed to be more effective than token scaling alone: a 20-candidate spectrum search with a larger token budget scored below the full four-agent framework.
  • The synthesis layer, not candidate generation, is the current bottleneck: closing the gap between oracle coverage and recovered accuracy is presented as the next target.
  • Strategic agent pruning can cut token use by up to 85% and sometimes improve accuracy, implying PoTRE is tunable along a cost-performance frontier.

Where Pith is reading between the lines

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

  • The 'verification bottleneck' suggests a testable extension: replacing the language-model synthesizer with an independent verifier (executable checks, formal solvers, or lookahead) could recover more of the oracle gap without adding reasoning agents.
  • Because synthesis fails when all agents disagree, the framework's ceiling may be set by candidate quality rather than selection; a future variant could add a fifth agent that generates a counter-argument or a fresh hypothesis only when divergence is high.
  • If the semantic-judge discrepancy noted in the paper's appendices favors PoTRE's evaluation, the headline 'heterogeneity beats homogeneous scaling' claim on HLE would need re-testing with identically graded baselines; this is a direct consequence of the reported protocol mismatch, not a speculation about the method's value.
  • The architecture implicitly predicts that benchmark performance will correlate with inter-agent answer divergence; a simple test is to track divergence bucket accuracy on other datasets and see whether synthesis gains concentrate in the moderate-divergence buckets as reported.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The paper introduces PoTRE (Poly-Topological Reasoning Ensembles), a heterogeneous multi-agent test-time reasoning framework. Four agents—Adversarial Refinement, Hierarchical Strategic Planning, Spectrum Search, and Direct Chain—generate candidate solutions in parallel, and a task-adaptive Synthesis agent aggregates them via candidate selection, semantic synthesis, or neuro-symbolic verification. The authors evaluate on ARC-AGI-2, Humanity's Last Exam (HLE), and PRBench Finance with Gemini-3-Flash/Pro/3.1-Pro, Claude-4.5-Sonnet, and DeepSeek backbones. The central claims are: (i) PoTRE achieves state-of-the-art HLE accuracy (49.92% closed-book, 58.40% open-book on the full set); (ii) heterogeneous scaffolding lets the smaller Flash model outperform the larger Pro baseline; and (iii) this is achieved with similar or fewer inference tokens than homogeneous scaling. Extensive ablations study agent contributions, oracle gaps, minority recovery, answer divergence, prompt robustness, and cost-performance trade-offs.

Significance. If the headline results are valid, the paper would provide a practically important demonstration that architecturally heterogeneous scaffolding can substitute for parameter scale and homogeneous sampling, and it would introduce a new state of the art on HLE. The paper is strong in the breadth and honesty of its ablation work: it reports oracle upper bounds, identifies a verification bottleneck, documents minority-recovery rates, includes cross-model validation beyond the Gemini family, conducts a human-judge concordance study, measures stochastic variance, and includes a controlled comparison against a 20-candidate homogeneous Spectrum Search baseline. These are real strengths that go beyond typical leaderboard reporting. However, the central comparison to Self-Consistency baselines on HLE is compromised by an evaluation-protocol asymmetry, and several internal ablations contradict the claim that the full four-agent ensemble is the best configuration. For these reasons the paper currently overstates the conclusiveness of its main message.

major comments (5)
  1. [Appendix A.7, Appendix F, Table 1] The HLE comparison between PoTRE and the Self-Consistency (SC) baselines is not methodologically commensurate. PoTRE is scored with the semantic LLM-as-a-judge described in Section 4.3, whereas Appendix A.7 states that for HLE the SC final prediction is selected by exact-string majority voting. Appendix F documents that exact matching grossly undercounts correct answers: on a 100-task sample, exact match gives 34% while the LLM judge gives 57%. If the same gap applies to the SC baselines, the reported SC numbers in Table 1 (e.g., 39.12% for Flash N=8) are deflated relative to PoTRE's 39.80%, and the 'heterogeneity beats homogeneous scaling' claim could flip. This is load-bearing. The authors should re-judge the SC output pools with the same semantic judge and report both exact-match and judged accuracy.
  2. [Section 6, Table 17, Table 25, Table 26] The 'similar or fewer tokens' claim is not substantiated by the reported data. Table 17 shows PoTRE-Flash consuming 680.5M output tokens versus an estimated 215.8M for ReThinker, which is roughly 3x more output tokens, not 'similar or fewer.' Table 25 shows PoTRE at 340K tokens/task versus 390K for a 20-candidate Spectrum Search baseline on HLE, but Table 26 shows PoTRE at 260K tokens/task versus 119K for a 4-candidate CoT ensemble. Moreover, the SC baselines in Table 1 are never given token counts. The claim needs to be scoped precisely: against which baselines and under which evaluation protocol is PoTRE token-competitive? The ReThinker cost comparison is also estimated rather than measured, and this should be stated in the main text, not only in the table caption.
  3. [Appendix G.2, Section 5.3] There is an internal inconsistency in the ablation narrative. Appendix G.2 reports that dropping the Standard Chain Agent on HLE yields 40.60% accuracy, which is higher than the full PoTRE result of 39.80% in Table 1; similarly, dropping the Hierarchical Strategic Planning Agent on ARC-AGI-2 gives 39.20% vs. the full system's 38.33%. Yet Section 5.3 claims the PoTRE Final Synthesis 'strictly and consistently outperforms the best individual agent,' and Section 7 recommends the full four-agent ensemble as the robust general-purpose configuration. The paper itself thus shows that the full ensemble is not the best configuration on either benchmark. This does not invalidate the framework, but the main text should acknowledge that the best configuration on the reported benchmarks is a pruned variant, and the headline numbers should not be presented as the optimal result unless a principled sel
  4. [Section 5.1, Section 5.6, Table 9] The 'state-of-the-art' claim on HLE is not directly supported. The open-book comparison in Table 9 is made on the HLE text-only subset (N=2,158) against Yunque DeepResearch and ReThinker, while the full-set number (58.40%) is compared only to the standalone Pro baseline in Section 5.6.1. The abstract's statement that PoTRE 'surpasses the previous best official score' requires a citation to the official HLE leaderboard and an explicit comparison of the same evaluation set and protocol. Similarly, the claimed ARC-AGI-2 Pass@2 'state-of-the-art 86.66%' is on the public evaluation set (Section 5.2.3), not the semi-private leaderboard; please clarify this distinction in the abstract and conclusion.
  5. [Sections 4.4, 5.2, Appendix G.2] The paper does not describe any validation procedure for the many task-adaptive choices: agent count, Spectrum Search N=8, adversarial turns T=5, hierarchical turn budgets, synthesis modes, and especially the agent-pruning decisions in Appendix G.2. Since these are tuned per benchmark and evaluated on the same public test sets, there is a risk that the reported gains reflect test-set selection. The authors should state which choices, if any, were made before seeing test results, and ideally report performance under fixed default hyperparameters as the primary result.
minor comments (6)
  1. [Section 4.2] The 'Baseline Models' paragraph is duplicated verbatim. Please remove the duplicate.
  2. [Appendix A.1] The Spectrum Search agent description says 'we generate N 4 parallel independent candidates' but later states N=8 and 'For all datasets in our implementation, we set N=8.' This is confusing; please correct the typo.
  3. [Section 5.2.3, Table 4] Pass@2 results are presented as 'state-of-the-art' without naming the previous public-eval-set record. Please state the exact comparison baseline and citation.
  4. [Section 3.2] The three synthesis modes are called 'Final Candidate Synthesis,' 'Qualitative Synthesis,' and 'Logic-Consistency Verification' in the text, but Algorithm 1 uses 'Candidate_Selection,' 'Qualitative_Synthesis,' and 'Neuro_Symbolic_Verifier.' Align the terminology.
  5. [Figure 4 and Figure 5] The text alternates between 'Standard Chain Agent' and 'Direct Chain Agent' when referring to the same component. Standardize on one name.
  6. [Appendix F] The human-subject evaluation reports 55% human accuracy and 57% LLM-judge accuracy with 98% item-level agreement. This is logically possible but surprising; please report the confusion matrix or explain how judge and human disagree on items where both accuracies differ.

Circularity Check

0 steps flagged

No significant circularity: PoTRE's results are empirical measurements against external benchmarks, not derivations that reduce to their inputs. The HLE evaluation-protocol asymmetry is a fairness/correctness risk, not a circular step.

full rationale

PoTRE is an empirical systems paper: no reported accuracy is obtained by solving an equation that already contains that accuracy as a definitional input. The four agents are independently prompted configurations, the synthesis layer is an LLM call, and all headline numbers are measured against external official protocols (HLE's official LLM-judge prompt, ARC-AGI-2 programmatic validation, PRBench's official rubric). The central comparisons (PoTRE vs. Self-Consistency, PoTRE vs. ReThinker/Yunque/Tongyi, and leave-one-out ablations) are experimental contrasts, not identities. The only author-overlap citations (SETS and TUMIX, co-authored by S. Ö. Arık) appear in Related Work as prior homogeneous-sampling frameworks that PoTRE contrasts with; neither is used to justify a uniqueness theorem, to derive an equation, or to forbid alternatives, so these self-citations are not load-bearing. The Appendix A.7/Appendix F issue (HLE Self-Consistency baselines aggregated by exact-string majority voting while PoTRE is graded by a semantic LLM judge) is a genuine evaluation-fairness risk that could affect the headline HLE comparison, but it is a benchmarking/correctness concern, not circularity: the SC numbers are not constructed from PoTRE's outputs and are not definitionally equal to PoTRE's inputs. Similarly, the ReThinker cost estimate anchors the baseline to the paper's own empirical token counts, but the paper explicitly labels it an estimate based on ReThinker's stated 128K context limit; modeling an unknown competitor's cost is not a prediction derived from PoTRE. No step in the paper's claimed derivation chain reduces to its own inputs, so the circularity score is 0.

Axiom & Free-Parameter Ledger

7 free parameters · 5 axioms · 0 invented entities

PoTRE introduces software agents and orchestration roles, but no new physical or formal entities. The central claim depends on manually chosen hyperparameters, task-dependent aggregation modes, and evaluation-protocol assumptions. The most fragile assumption is that the Self-Consistency baselines were scored fairly, which is contradicted by the exact-match protocol in Appendix A.7.

free parameters (7)
  • ensemble agent count = 4
    Architecture uses exactly four reasoning agents; no search over this number is reported, and leave-one-out shows different subsets are optimal per benchmark.
  • Spectrum Search candidate count (N) = 8
    Set N=8 for all datasets (Appendix A.1); not swept.
  • Adversarial Refinement max turns (T) = 5
    Debate loop capped at 5 turns (Section 4.4); not swept.
  • Hierarchical agent turn budget = 1 turn (HLE/PRBench), 10 turns (ARC-AGI-2)
    Manual task-dependent budget.
  • Sampling temperatures = generation 0.7; judge 0.0; ARC solver 0.2-1.0; baselines 0.0
    Chosen by hand for each role; no rigorous tuning reported.
  • Task-adaptive aggregation mode = HLE=Final Candidate Selection, PRBench=Qualitative Synthesis, ARC=Neuro-Symbolic Verifier
    The synthesis strategy for each benchmark is assigned by the authors based on their task-type labels, not learned or ablated.
  • Search augmentation on/off = Open-book: Google Search enabled; Closed-book: disabled
    The paper evaluates both; the open/closed mode is a manual experimental condition.
axioms (5)
  • domain assumption HLE LLM-as-a-judge (Gemini-3-Flash-Preview) correctly measures semantic equivalence of responses.
    HLE accuracies in Table 1 rely on this judge; Appendix F validates on 100 tasks (98% human agreement) but only for a sample, and the judge is from the same model family as the main backbones.
  • domain assumption The four role-specific prompt templates induce reasoning paths that are sufficiently distinct to count as different topologies.
    Sections 3.1 and 5.8 assume this; divergence rates in Table 8 and complementarity ratios in Table 16 support it, but the distinction is prompt-level and model-dependent.
  • ad hoc to paper Self-Consistency baselines as implemented in Appendix A.7 are fair representatives of homogeneous test-time scaling.
    Appendix A.7 uses exact-string majority voting for HLE/ARC, while PoTRE is scored with an LLM judge; this is an unfair comparison assumption that weakens the central claim.
  • domain assumption Official evaluation protocols for ARC-AGI-2 (programmatic validation) and PRBench (rubric-based script) are reliable.
    Section 4.3 relies on them; PRBench is an internal benchmark with no public leaderboard details.
  • domain assumption API model outputs are stable across time and calls.
    Reproduction depends on proprietary API; no snapshots or code are provided.

pith-pipeline@v1.3.0-alltime-deepseek · 44302 in / 16353 out tokens · 146388 ms · 2026-08-01T10:17:53.358974+00:00 · methodology

0 comments
read the original abstract

While Large Language Models (LLMs) excel at many tasks, they frequently struggle with complex reasoning that requires long-horizon planning and iterative error correction. Furthermore, standard single-stream prompting proves brittle when models encounter novel abstractions or rigorous domain constraints. We introduce PoTRE (Poly-Topological Reasoning Ensembles), a heterogeneous framework that decouples inference into four agents: (1) Adversarial Refinement Agent, (2) Hierarchical strategic Planning Agent, (3) Spectrum Search Agent, and (4) Direct Chain Agent. A final Task-Adaptive Aggregation Layer dynamically reconciles these perspectives -- via final candidate selection, semantic synthesis, or neuro-symbolic verification -- to produce a robust global solution. We evaluate PoTRE on three frontier benchmarks: ARC-AGI-2, Humanity's Last Exam (HLE), and PRBench Finance. PoTRE achieves state-of-the-art accuracy of 49.92% on HLE, surpassing the previous best official score. We demonstrate that this architectural heterogeneity achieves improved reasoning performance using similar or fewer inference tokens compared to heavily scaled homogeneous baselines.

Figures

Figures reproduced from arXiv: 2607.20268 by Anmol Kankariya, Sercan \"O. Ar{\i}k.

Figure 1
Figure 1. Figure 1: Comparison of the smaller Gemini-3-Flash-Preview model equipped with PoTRE (Blue) against [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Poly-Topological Reasoning Ensembles. This diagram illustrates the proposed multi-agent reasoning process. An input problem is processed in parallel by four distinct agent-based strategies: (1) Adversarial Refinement, involving iterative debate for high-precision output, (2) Hierarchical Strategic Plan￾ning, using a nested team structure for structured solutions, (3) Spectrum Search, generating and selecti… view at source ↗
Figure 3
Figure 3. Figure 3: Impact of Search Augmentation on HLE. Performance comparison of the four sub-agents under Closed-Book and Search-Augmented constraints. External retrieval universally elevates accuracy across all reasoning topologies. Notably, the Direct Chain Agent experiences the most dramatic marginal gain (+14.60%), effectively utilizing search to overcome its structural limitations and matching the performance of more… view at source ↗
Figure 4
Figure 4. Figure 4: Cost-Performance Pareto Frontier on HLE. Leave-one-out ablation results on the HLE dataset. The dashed line illustrates the optimal Pareto front. Dropping the Spectrum Search Agent maxi￾mizes token efficiency, while dropping the Standard Chain Agent achieves the highest overall accuracy. HLE Dataset Analysis: As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p067_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Cost-Performance Pareto Frontier on ARC-AGI-2. Leave-one-out ablation results on the ARC-AGI dataset. Counter-intuitively, the full 4-agent PoTRE configuration is sub-optimal; strategically pruning the Hierarchical Strategic Planning Agent reduces token consumption by 84% while simultaneously improving accuracy. 68 [PITH_FULL_IMAGE:figures/full_fig_p068_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Impact of Synthesis Layers on HLE Accuracy (No Search). Comparison of different candidate [PITH_FULL_IMAGE:figures/full_fig_p070_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Synthesis Method Comparison on PRBench Finance. Performance of different aggregation [PITH_FULL_IMAGE:figures/full_fig_p071_7.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

92 extracted references · 1 linked inside Pith

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    **Strategic Pivot:** Dynamically synthesize a high-level strategic directive to re-orient the Architect’s approach. OUTPUT FORMAT (JSON): { "diagnosis": "The agent keeps trying to calculate X without considering Y...", "custom_advice": "You are ignoring the constraint about ’without moving queens’. Focus on knight moves." } Interaction Templates The follo...

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    **Clarity**: Is it easy to understand? Output JSON: { "best_candidate_index": int } Input Template: Judge’s ViewThe Judge receives the full conversation history (User turns + final question). Then, the candidate answers are appended to the final user turn. Input: Judge Context [History...] [User]: {current_question} [Optional Image Attachment] Here are th...

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    Completeness: Did the answer address the user’s question fully? If the answer is accurate and complete, APPROVED it. If not, provide specific critique demanding the missing or incorrect elements. Output JSON: { "critique": "...", "status": "APPROVED" or "REJECTED" } •Dynamic Context & FlowThe context is constructed linearly. The agent sees the following sequence:

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    UNIVERSALITY: The rule must apply to ALL training pairs exactly the same way. - REJECT: "If Pair 1 do X, If Pair 2 do Y." - ACCEPT: "For ALL inputs, find the object with the most unique colors and rotate it 90 deg ."

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    BI-DIRECTIONAL CHECK (Mental): - Can you reconstruct the Input from the Output? If not, is information lost? (e.g. projection) - Describe the transformation as a precise algorithm. - DO NOT just predict pixels. Predict the FUNCTION. --- RESPONSE GUIDELINES ---

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    DO NOT WRITE python CODE YET

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    FOLLOW THE FORMAT BELOW EXACTLY

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    Format: [Analysis]

    Your Hypothesis must be natural language. Format: [Analysis]

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    Refined Algorithm

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    CRITIQUE: What is WRONG with your Turn 1 Hypothesis? (Edge cases? Ambiguity?)

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    If your logic is a page long, IT IS LIKELY WRONG

    SIMPLICITY CHECK: ARC tasks are usually solved by 1-3 simple core rules. If your logic is a page long, IT IS LIKELY WRONG. Can you simplify? 46 Published in Transactions on Machine Learning Research (07/2026)

    2026

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    REFINE: precise natural language algorithm

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    - If it fails even one pixel on Example 1, IT IS WRONG

    MENTAL VERIFICATION: Walk your hypothesis through ALL training examples. - If it fails even one pixel on Example 1, IT IS WRONG. Discard it

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    [Refined Algorithm]

    STATUS CHECK: Are you confident this algorithm is perfect? Format: [Critique] ... [Refined Algorithm] ... [Status] CONTINUING (or READY) •Subsequent Iteration Prompt Prompt: Critique & Refinement (Turn 2, Iteration N) PREVIOUS REFINEMENT: [CONTEXT] YOUR TASK:

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    Did it fix the issues? Any new edge cases?

    CRITIQUE the Previous Refinement. Did it fix the issues? Any new edge cases?

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    Think in terms of Objects and Physics (Gravity, Collision, etc)

    SIMPLICITY CHECK: Is it still too complex? Try to cut 50% of the steps. Think in terms of Objects and Physics (Gravity, Collision, etc)

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    Refined Algorithm

    STATUS CHECK: Are you confident now? •Phase 3: The Solver (Implementation) –Goal: Translate the validated "Refined Algorithm" into a self-contained python function. –Temperature: Uses a "temperature ladder" ranging from 0.2 to 1.0, distributed linearly across k= 8parallel candidates to ensure a diverse mix of conservative and creative code implemen- tatio...

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    Implement the solution using your Validated Hypothesis and Algorithm

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    Focus on: Extract Object -> Transform (Rotate/Flip/Align) -> Result

    Simplify your logic. Focus on: Extract Object -> Transform (Rotate/Flip/Align) -> Result

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    Write the python function ‘transform(grid)‘

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    DO NOT assume any external helper functions serve you

    The code MUST be self-contained. DO NOT assume any external helper functions serve you

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    Output ONLY valid python code in a code block

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    --- VISUAL CONTEXT ---

    Use the ‘transform‘ function signature. Temperature: Fixed at 0.2 to ensure precise, non-random modifications when fixing code errors. Repair Code You are an expert in solving Abstract Reasoning Corpus tasks., GOAL: Fix the Python Code which fails on the Training Examples., "--- VISUAL CONTEXT ---", 47 Published in Transactions on Machine Learning Researc...

    2026

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    LOOK at the images and the error feedback.,

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    Is it a Logic Error, Shape Mismatch, or Pixel Mismatch? Why did it happen?",

    THINK STEP-BY-STEP: Analyze the Error. Is it a Logic Error, Shape Mismatch, or Pixel Mismatch? Why did it happen?",

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    GENERALIZATION STRATEGY: How can you fix this logic so it works for ALL examples, not just this specific failure?",

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    IMPLEMENTATION: Return the fixed Python code.,

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    show its work

    Output Format:\n[Analysis]\n...\n[Code]\n‘‘‘python\n...\n‘‘‘ A.4.4 Direct Chain Agent (ARC-AGI) •Turn 1: Analysis & Verifiable Hypothesis –Goal: Force the model to "show its work" by interacting with the python environment to verify hypothesesbeforecommitting to a solution. The model must implement a testing loop where it discards wrong hypotheses. –Tempe...

    2026

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    For example, if one candidate has better calculations, use them

    **Enrichment**: Dynamically select the best elements from ANY candidate. For example, if one candidate has better calculations, use them. If another has better citations or formatting, use those. Do not assume specific candidates have specific strengths

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    **Completeness**: Ensure every single user question is answered with maximum depth

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    **Accuracy**: If candidates have conflicting numbers, perform a sanity check and use the most plausible/consistent one

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    **Tone**: Professional, authoritative, and precise

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    Do not explain your process

    **Output**: Provide ONLY the final merged answer. Do not explain your process. Final Answer: System Prompt: ARC-AGI-2 You are a Principal Logic Expert and ARC-AGI Judge. You are tasked with determining the Single Correct Output Grid for a given Test Input. You have access to:

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    **Problem Description**: Training examples demonstrating the rule

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    GOAL: Synthesize the perfect solution

    **Candidates**: Proposed solutions from other expert agents. GOAL: Synthesize the perfect solution. - The candidates might be correct, partially correct, or completely wrong. - Use the Training Examples to VERIFY the rule. - If a candidate follows the rule perfectly, select it. - If candidates diverge, use your reasoning to correct the errors. - If all ca...

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    You MUST plan extensively before generating the final answer

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    Reflect on your reasoning at each step to catch potential errors

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    # Task Execution Rules:

    Do not just guess; derive the answer through logical deduction. # Task Execution Rules:

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    50 Published in Transactions on Machine Learning Research (07/2026) - Identify what information is given and what needs to be inferred

    Analysis: - Break down the problem into key components. 50 Published in Transactions on Machine Learning Research (07/2026) - Identify what information is given and what needs to be inferred

    2026

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    - For each step, explicitly state your reasoning and the conclusion of that step

    Step-by-Step Reasoning: - Execute your plan step by step. - For each step, explicitly state your reasoning and the conclusion of that step. - If a step involves calculation or logic, double-check it

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    Does this make sense?

    Reflection: - Before concluding, ask yourself: "Does this make sense?", "Is there a counter-example?", "Did I miss any constraints?"

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    rationale

    Final Output: - Provide the final answer clearly at the end. - If the question asks for a specific format (e.g., a number, a code snippet), strictly follow it. # Environment Information - You are running in a restricted reasoning environment without external tools (no web search , no code execution). - Rely solely on your internal knowledge and logical ca...

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    Double-check your arithmetic

    PRECISION: Perform all calculations with high precision. Double-check your arithmetic

Showing first 80 references.