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Amplified Does Not Mean Predictive: Reasoning Behaviors in Thinking Models
Jean de Dieu Nyandwi, Leena Mathur, Yonatan Bisk, Robert Hawkins, Graham Neubig
TL;DR
Reasoning-oriented training can make traces look more deliberative, but it is unclear whether it amplifies behaviors associated with correctness. This paper measures behavior–correctness associations across reasoning traces and finds that thinking models amplify behaviors other than those most associated with correctness.
Problem
Answer accuracy does not show which reasoning behaviors models use or whether behaviors common in thinking traces are associated with correctness.
Source: §1 · p. 1Method
Results
Thinking models amplify self-correction, hypothesis testing, and uncertainty acknowledgment rather than the highest-lift behaviors; confidence calibration is among the strongest correctness signals in both modalities (+72–80%).
Source: §1 · p. 2, §7 · p. 10Takeaways & Limitations
Process objectives should reward behaviors associated with correctness, such as grounded claims and calibrated confidence, rather than deliberative surface markers alone.
Source: §5 · p. 9The analysis is limited to visible traces, which may be incomplete, post hoc, or unfaithful, and Behavioral Lift is descriptive rather than causal.
Source: §5 · pp. 9–10
Abstract
from arXiv · showhide
Which reasoning behaviors are associated with correct answers in reasoning models, and does reasoning-oriented training amplify those behaviors? This distinction is important because reasoning-oriented training can make traces look more deliberative without amplifying the behaviors most tied to model correctness. We quantify this mismatch with Behavioral Lift, a metric that measures how much correctness changes when a behavior is present versus absent in a model's reasoning trace. Across 15 models and 6 benchmarks spanning text-only and vision-language reasoning, we annotate 15,282 traces with a taxonomy whose core behaviors are defined for both LLM and VLM traces. We find evidence for an Amplification-Lift Gap, in which thinking models strongly amplify self-correction, hypothesis testing, and uncertainty acknowledgment, while the highest-lift behaviors are confidence calibration, knowledge alignment, and self-awareness. Confidence calibration is among the strongest positive signals of correctness in both modalities, yet is barely amplified; uncertainty acknowledgment is amplified by 3--7$\times$, yet is weakly or negatively associated with correctness. We find that reasoning-oriented training does not preferentially amplify the highest-Lift behaviors, motivating process-level objectives that reward calibrated and grounded reasoning rather than surface form alone.
1 Introduction
The paper separates how often reasoning behaviors appear from how strongly they are associated with correct answers. It finds that thinking-oriented training amplifies some behaviors without preferentially amplifying the behaviors most associated with correctness.
1 Introduction
- Answer accuracy alone does not reveal which reasoning behaviors a model used or whether behaviors common in thinking traces are associated with correctness.Behaviors can become more frequent without becoming more diagnostic of successful reasoning; confidence calibration and applying the appropriate domain framework may be less prevalent yet more predictive.Source: §1 · p. 1
- +72–80%: confidence calibration is among the strongest correctness signals in both modalities, yet thinking models amplify self-correction, hypothesis testing, and uncertainty acknowledgment instead.Uncertainty acknowledgment is weakly or negatively associated with correctness, and no behavior is both strongly amplified and strongly predictive of success.Source: Figure 1 · p. 2, §1 · p. 2
- The study defines Behavioral Lift and Recovery Rate, and applies a cross-modal taxonomy to 15,282 traces from 15 models across six benchmarks.Behavioral Lift measures the correctness difference when a behavior is present versus absent; Recovery Rate measures reaching a correct answer despite at least one reasoning failure.Source: §1 · p. 2
- Thinking models recover better on extended-reasoning tasks and make fewer failures on knowledge-heavy tasks, while instruct models can outperform when fast recognition of logical form suffices.The paper identifies recovery from failures and extended computation as conditions under which thinking helps.Source: §1 · p. 2
2 Taxonomy and Metrics
The section introduces a cross-modal taxonomy of reasoning behaviors and failure modes, plus Behavioral Lift and Recovery Rate to describe reasoning quality. It distinguishes observable behaviors from their association with correctness and from successful answers despite detected failures.
2.1 Design Principles
- The taxonomy annotates reasoning processes as well as answers, defines higher-order behaviors in modality-neutral terms, and distinguishes behavior presence from Behavioral Lift.This distinguishes valid reasoning from lucky guesses and recognizes that frequently present behaviors are not necessarily most associated with success.Source: §2.1 · p. 3
2.2 Taxonomy Structure
- The taxonomy’s six groups include nine modality-neutral higher-order behaviors for cross-modal analysis and failure modes for Recovery Rate analysis.The other groups capture reasoning quality, reasoning types, trace summaries, and VLM visual grounding. The nine behaviors cover control/regulation, monitoring/judgment, and epistemic grounding.Source: §2.2 · p. 3, §2 · p. 3
- Failure modes are annotated as present or absent, with four shared across modalities and three additional modality-specific modes for each.The shared modes are logical failure, post-hoc rationalization, shortcut, and lucky guess; explicit failure annotation supports analysis of correct answers despite failures.Source: §2.2 · p. 3
2.3 Metrics
- Behavioral Lift measures how correctness differs when a behavior is present versus absent; positive and negative Lift indicate higher and lower accuracy, respectively.It is descriptive, not causal, and is computed on pooled samples by modality, with supplementary analyses assessing stability within benchmarks and models.Source: §2.3 · p. 4
- Recovery Rate measures whether a model reaches correct answers despite detected reasoning failures.The failure set includes all seven modes for the relevant modality.Source: §2.3 · p. 4
3 Experimental Setup
The setup compares matched thinking and instruction-tuned models across reasoning-to-knowledge task spectra in both text and vision-language settings, with full traces retained for behavioral analysis.
3.1 Models
- The evaluation includes 15 open-weight models (3B–9B parameters), with matched thinking and non-thinking variants within model families wherever available.There are 4 thinking and 4 non-thinking LLMs, and 4 thinking and 3 non-thinking VLMs.Source: §3.1 · p. 4
3.2 Benchmarks
- Benchmarks cover visual reasoning, visual mathematics, and multimodal knowledge for VLMs, and logical reasoning, mathematics, and knowledge-intensive tasks for LLMs.The benchmarks are VisualPuzzles, MathVista, and MMMU for VLMs, and LogiQA2, MATH-500, and MMLU-Pro for LLMs; MMLU-Pro spans 14 domains.Source: §3.2 · pp. 4–5
- 15,282 responses were annotated, with targets of 350 per benchmark except 300 MathVista testmini responses.Final counts fell slightly below targets because responses that failed output parsing were excluded.Source: §3.2 · p. 5
3.3 Evaluation Protocol
- Responses were evaluated under standardized conditions, and GPT-4o assigned binary behavior labels from each question, ground-truth answer, and full model output.For confidence calibration, the judge labeled certainty true only when it matched the strength of reasoning in the trace; judge validation found 83.7%–88.8% mean agreement across behaviors and 95.1% agreement on 720 manual-check decisions for six focal behaviors.Source: §3.3 · p. 5
4 Results
Thinking models amplify correction, hypothesis testing, and uncertainty more than the behaviors most associated with correctness. Their accuracy advantages are task-dependent and can arise from recovering after failures.
4.1 Thinking-Oriented Models Amplify Correction, Search, and Hesitation
- Thinking models show self-correction in 21–55% vs 3–15% of responses, with hypothesis testing and uncertainty acknowledgment similarly amplified across seven model families, two modalities, and six benchmarks.Hypothesis testing appears in 22–52% vs 4–18%, and uncertainty acknowledgment in 25–85% vs 4–28%. Confidence calibration is roughly equal between thinking and instruct models, while planning and goal tracking are nearly identical on MATH-500 and MMLU-Pro.Source: §4.1 · p. 5
4.2 The Most Amplified Behaviors Are Not the Highest-Lift Behaviors
- Confidence calibration has +72.2% Lift (VLM) and +79.6% (LLM), while amplified uncertainty acknowledgment has −16.1% and −13.9%, respectively.Confidence calibration, self-awareness, and knowledge alignment rank highest across modalities, while the amplified behaviors rank lowest. Within-question analysis also found a +0.31 thinking-model advantage for confidence calibration and +0.01 for uncertainty acknowledgment.Source: §4.2 · pp. 6–7, Figure 3 · p. 7
4.3 Thinking Models Often Succeed Through Recovery
- On VisualPuzzles, MATH-500, and MMLU-Pro, thinking models recover 2.7×, 2.3×, and 2.7× as often, but instruct models recover better on LogiQA2.Self-correction is associated with +39pp recovery among traces with a detected failure; fewer failures or better recovery yield higher thinking-model accuracy on 4 of 6 benchmarks. On LogiQA2, instruct models outperform thinking models (58.4% vs 54.1%) by taking more successful shortcuts (34.2% vs 20.3%).Source: §4.3 · p. 8
4.4 OLMo-3 SFT Show the Amplification–Lift Mismatch Before DPO/RLVR
- Before DPO/RLVR, OLMo Think-SFT amplified self-correction (34.5% vs 1.0%), yet knowledge alignment and confidence calibration had the highest within-checkpoint Lift.Knowledge alignment and confidence calibration Lift were +81.0%/+67.4% in Think-SFT and +81.4%/+81.3% in Instruct-SFT; the comparison does not isolate any single training stage’s causal contribution.Source: §4.4 · p. 8
4.5 The Amplification-Lift Gap Persists at Scale
- At Qwen3-VL 32B, self-correction remained amplified (61.3% vs 11.3%), while confidence calibration favored instruct (78.0% vs 71.7%).Self-correction Lift fell from +30.0% at 2B to +5.9% at 32B, while confidence calibration Lift rose from +57.7% to +68.7%. Uncertainty acknowledgment fell from 66.7% to 43.7% in thinking models, supporting the interpretation that it tracks difficulty rather than calibration.Source: §4.5 · pp. 8–9
5 Discussion
Behavioral Lift distinguishes frequent deliberation from behaviors associated with correctness: confidence calibration tracks correctness, while explicit uncertainty alone does not. The discussion argues for process objectives that reward grounded, calibrated reasoning, while noting limits of visible-trace analysis and causal interpretation.
5 Discussion
- Behavioral Lift separates behavior frequency from usefulness: self-correction and uncertainty can signal either successful reasoning or earlier error and confusion.It measures how correctness differs when a behavior is present versus absent.Source: §5 · p. 9
- Confidence calibration tracks correctness, whereas explicit uncertainty acknowledgment is weakly or negatively associated unless doubt reflects the available evidence (Kim et al., 2026).Calibration means expressed certainty matches reasoning strength; saying “maybe” at every step is not calibration.Source: §5 · p. 9
- Thinking helps most when tasks reward extended computation or recovery from mistakes, but can hurt when fast recognition of logical form is sufficient.Evaluations should report whether thinking helps and how, including failure prevention, recovery, calibration, or changes in shortcuts; recent work targets inefficiency in long traces (Ma et al., 2025; Arora & Zanette, 2025).Source: §5 · p. 9
- Process objectives should reward evidence-grounded claims, appropriate domain framing, recognition of underspecified information, and confidence matched to reasoning strength.Long traces, backtracking, and expressed uncertainty are not objectives in themselves; Behavioral Lift can audit whether supervision rewards success-linked behavior or deliberative surface form.Source: §5 · p. 9
- The analysis is limited to visible traces, which may be incomplete, post hoc, or unfaithful, and automated labels may retain systematic judge bias despite validation and robustness checks.Behavioral Lift is descriptive: high-lift behaviors may cause better performance, reflect being on track, or co-occur with another useful property; prompting results align with the ranking, but controlled training studies remain necessary (Boppana et al., 2026; Chen et al., 2025; Baker et al., 2025).Source: §5 · pp. 9–10
6 Related Work
This related work connects reasoning-oriented training, behavioral analysis of reasoning traces, and process supervision. The paper distinguishes behaviors amplified by training from those associated with correctness and proposes correctness association as a criterion for process supervision.
6 Related Work
- Thinking-oriented models amplify self-correction, hypothesis testing, and uncertainty acknowledgment, but these are not the behaviors most associated with correctness.Prior work finds RL or hybrid post-training can elicit long traces with backtracking and self-correction; some abilities are present in base models but require substantial RL compute for reliable elicitation (Guo et al., 2025; Team et al., 2025; Yang et al., 2025; Chu et al., 2025; Yeo et al., 2025; Yue et al., 2025).Source: §6 · p. 10
- This work compares thinking and instruct variants across LLMs and VLMs, separating frequent behaviors from those with high Behavioral Lift.Related work analyzes cognitive behaviors, trace taxonomies, reasoning strategies, semantic flow, and trace instability (Gandhi et al., 2025; Kargupta et al., 2025; Lee et al., 2025; Chang et al., 2026; Wang et al., 2025).Source: §6 · p. 10
- Process supervision should reward a reasoning behavior only after testing whether it is associated with correctness.Chain-of-thought prompting can improve final-answer accuracy (Wei et al., 2023), while process supervision evaluates reasoning at the step level (Lightman et al., 2023); benchmarks and verifier-style methods test error detection and shortcut avoidance (Zheng et al., 2025; Zhong et al., 2025).Source: §6 · p. 10
7 Conclusion
Across 15,282 traces from 15 models and 6 benchmarks, thinking training amplified several reasoning behaviors without preferentially amplifying those most associated with correctness. The findings point toward training and evaluation that reward calibrated, grounded, and recoverable reasoning.
7 Conclusion
- Thinking training amplified self-correction, hypothesis testing, and uncertainty acknowledgment, while confidence calibration was a strong positive correctness signal and uncertainty acknowledgment was often non-predictive or negative.Several higher-lift behaviors remained largely unchanged despite their association with correctness.Source: §7 · p. 10
- The analysis quantified behavioral effects across 15,282 traces from 15 models and 6 benchmarks.Source: §7 · p. 10
- Future training and evaluation should reward calibrated confidence, grounded evidence use, appropriate domain knowledge, and recovery when reasoning goes wrong.Source: §7 · p. 10
Ethics Statement · A Inference Details · B Qualitative samples and surface markers of annotated behaviors
The paper frames its analysis as observational benchmark research, documents its inference procedures, and provides qualitative examples to make behavior labels auditable. It cautions that visible reasoning traces may not reflect internal reasoning and says it will release materials for reproducibility.
Ethics Statement
- The analysis covers benchmark responses rather than real-world deployment, and visible traces are not claimed to fully represent internal reasoning.The authors say they will release prompts, metrics code, and annotations to support transparency and reproducibility.Source: Ethics Statement · p. 11
- LLMs were used for grammar and editing, and to implement and verify selected analyses.The paper says LLM-based annotation is described in the methodology.Source: Ethics Statement · p. 11
A Inference Details
- Evaluations use lm-evaluation-harness for LLMs and lmms-eval for VLMs, with zero-shot prompting and benchmark-native CoT variants when available.Instruct-model prompts follow benchmark or evaluation-suite templates; the authors say they will release run scripts, subset files, prompt templates, and scoring code.Source: A Inference Details · p. 14, Table 3 · p. 15
- LLM evaluations used vLLM and family-specific decoding settings, with generation limits ranging from 8,192 to 32,768 tokens across the listed model variants.Reasoning-enabled models used temperature 0.6, top-p 0.95, top-k 20, and min-p 0; Qwen2.5 instruct used temperature 0.7, top-p 0.8, top-k 20, and min-p 0; Nemotron-Base used greedy decoding. Qwen scaling runs allowed 16,384 tokens, OLMo-3 and DeepSeek-R1-Distill 32,768, and Nemotron variants 8,192.Source: A Inference Details · p. 14
- VLM evaluations used vLLM-backend with family-specific settings, including different decoding regimes and generation limits for thinking and instruct variants.Qwen3-VL-8B and GLM-4.1V-9B used 40,960-token limits for thinking and 16,384 for instruct; Kimi-VL-A3B and InternVL3.5-8B used 16,000-token limits for both variants, with settings differing by family and variant.Source: A Inference Details · p. 14
- The evaluations retain framework wrappers and model-specific defaults without extra prompting designed to elicit particular behaviors.Table 3 summarizes the generation settings used for the main evaluations.Source: A Inference Details · p. 14, Table 3 · p. 15
B Qualitative samples and surface markers of annotated behaviors
- Qualitative samples make behavior labels auditable, while illustrating overlapping surface markers across behaviors.Uncertainty acknowledgment can use hedging; self-awareness can take the form of information audits; self-correction uses breaks in reasoning; and hypothesis testing uses branching. Confidence calibration often appears as a changing confidence arc, knowledge alignment as use of an appropriate domain framework, and goal tracking as stating what is established and what remains to solve.Source: B Qualitative samples and surface… · pp. 14–15
C Robustness to annotation noise and question difficulty
Across robustness checks, confidence calibration remains strongly associated with sound reasoning, while uncertainty acknowledgment remains weak or lowest-ranked. Calibration is also far less common on lucky guesses than on non-lucky responses.
Within-question analysis
- Same-question comparisons across Qwen3-4B-Think, Qwen3-4B-Instruct, and Qwen3-4B-VL on MathVista retain strongly positive calibration, smaller positive self-correction, weak hypothesis testing, and null-or-negative uncertainty acknowledgment.These comparisons constrain a question-difficulty explanation but do not resolve causal direction because latent reasoning-path quality may affect both behavior and correctness.Source: C Robustness to annotation noise and… · pp. 15–16
Sensitivity to random annotation noise
- Under random flips of 5%, 10%, 15%, and 20% of labels across 1000 trials, calibration remains top-ranked for VLMs and alongside knowledge alignment for LLMs; uncertainty acknowledgment stays lowest.Hypothesis testing is the only unstable behavior, and its clean Lift is already near zero.Source: C Robustness to annotation noise and… · p. 16
Lucky-guess analysis
- Calibration is far less common on lucky guesses than on non-lucky responses across LLMs and VLMs, and thinking and instruct models, suggesting it tracks sound reasoning beyond final-answer correctness alone.Lucky guesses have correct final answers despite flawed reasoning.Source: C Robustness to annotation noise and… · p. 16
Temporal position of behaviors
- Across sampled thinking-model traces, all four behaviors typically appear before the answer (91–99% of traces), with mean behavior positions of 27–35% versus 79–94% for the answer (Chang et al., 2026).This shared pattern reflects the general structure of reasoning traces, not a property unique to calibration.Source: C Robustness to annotation noise and… · p. 16
D Length-Controlled Prevalence Analysis
Controlling for response length reduces some observed amplification, especially in the shortest traces, but does not eliminate the pattern: several behaviors remain more prevalent in thinking-oriented traces across longer word-count bins.
D Length-Controlled Prevalence Analysis
- After length control, self-correction, hypothesis testing, and uncertainty acknowledgment remain amplified across most non-shortest LLM bins; for VLMs, amplification persists in 4/5, 3/5, and 5/5 bins, respectively.The analysis compares thinking-minus-comparison prevalence gaps within shared word-count quintiles in each modality.Source: D Length-Controlled Prevalence Analysis · p. 16
- Length accounts for part of the amplification pattern, especially among the shortest traces, but response length alone does not explain it.The conclusion uses a trace-level definition of amplification.Source: D Length-Controlled Prevalence Analysis · p. 16
- The analysis recomputes thinking-minus-comparison prevalence gaps within shared word-count quintiles separately for LLM and VLM traces.Source: D Length-Controlled Prevalence Analysis · p. 16
E Linear Probing of Behavioral Representations · F Prompting Behavioral Lift at Inference Time
Linear probes show that several annotated behaviors are represented in model hidden states, while inference-time prompting supports the Behavioral Lift ranking: high-Lift prompts generally improve accuracy, whereas pervasive uncertainty prompting reduces it. The prompting evidence is preliminary because each prompt changes several trace properties at once and GPQA differences are not statistically significant.
E Linear Probing of Behavioral Representations
- Probes used teacher-forced replay of saved traces, layer 35 of 36 generated-token hidden states, and logistic regression with question-grouped cross-validation to prevent leakage.The analysis compared Qwen3-4B-Thinking and Qwen3-4B-Instruct.Source: E Linear Probing of Behavioral… · p. 16
- In Qwen3-4B-Thinking, probe decodability aligns with Behavioral Lift most strongly on incorrect-only traces (ρ = +0.86, p = 0.014), while confidence calibration is decodable on correct-only traces in both models.Among incorrect thinking traces, knowledge alignment and goal tracking were among the most separable behaviors, while hypothesis testing and uncertainty acknowledgment were among the least separable.Source: E Linear Probing of Behavioral… · pp. 16–17
- On incorrect Qwen3-4B-Instruct traces, probe decodability correlated negatively with Lift (ρ = −0.71, p = 0.071), with hypothesis testing and uncertainty acknowledgment among the most decodable behaviors.Confidence calibration occurred in only 1.7% of incorrect thinking traces and 0.7% of incorrect instruct traces, preventing reliable probe evaluation in that subset.Source: E Linear Probing of Behavioral… · p. 17
F Prompting Behavioral Lift at Inference Time
- High-Lift prompts increased confidence calibration (+0.4 to +4.4pp) and knowledge alignment (+3.8 to +8.0pp), while Low-Lift prompts drove uncertainty acknowledgment to 94.4–99.3%.High-Lift prompts encouraged confidence calibration, knowledge alignment, and self-awareness; Low-Lift prompts encouraged pervasive uncertainty acknowledgment, the most amplified behavior with consistently negative Lift.Source: F Prompting Behavioral Lift at… · pp. 17–18
- On MATH-500, High-Lift prompting raised recovery from 57.1% to 68.9% and reduced logical failure, context misread, and knowledge gap rates.The effect scaled with baseline competence: MATH-500 baseline accuracy was 84.8%, MMLU-Pro Math was 58.4%, and GPQA was near chance at 49.1%.Source: F Prompting Behavioral Lift at… · pp. 17–18
- Across three benchmarks, High-Lift > Baseline > Low-Lift; gains were strongest on MATH-500 (+5.8pp), while GPQA’s +0.7pp was nonsignificant.High-Lift versus Low-Lift gaps were +10.4pp on MATH-500 (p < 0.001), +15.7pp on MMLU-Pro Math (p < 0.001), and +4.3pp on GPQA (p = 0.086). On MMLU-Pro Math, Low-Lift reduced accuracy by 12.1pp (p < 0.001); on GPQA, Low-Lift reduced accuracy by 3.5pp (p = 0.14).Source: F Prompting Behavioral Lift at… · pp. 17–18, Table 5 · p. 18
- The inference-time experiment is preliminary: prompts changed several trace properties simultaneously, so it cannot isolate any single behavior’s effect, and GPQA differences were not statistically significant.The authors present the results as evidence that the Lift ranking carries usable signal at generation time and identify controlled training interventions as a next step.Source: F Prompting Behavioral Lift at… · p. 18
- The Lift ranking persisted across response-complexity bins: confidence calibration remained strongly positive and uncertainty acknowledgment negative in both modalities, independent of response length or problem difficulty.The passage also reports consistent results from a word-count-quintile analysis.Source: Figure 4 · p. 19
G Visual Claim Accuracy Matters More Than Image References
This section examines how visual-claim accuracy and reasoning behaviors relate to correctness in VLM traces, with examples of evidence-grounded checks, uncertainty, and correcting derivation errors. It shows that accurate visual interpretation and resolving errors are informative, while merely mentioning image content or deliberating does not ensure correctness.
G Visual Claim Accuracy Matters More Than Image References
- +60.9 Behavioral Lift links accurate visual claims to 87.8% correctness versus 26.9% for inaccurate claims, while visual-reference presence has -3.3 Lift overall.Accuracy is strongly positive on VisualPuzzles (+71.7), MathVista (+54.3), and MMMU (+45.1); reference presence is near zero except on MathVista (+12.1).Source: G Visual Claim Accuracy Matters More… · p. 19
- Thinking models show less visual neglect (36.0% vs. 47.4%) but more visual hallucination (24.7% vs. 18.0%) than instruct models.They recover better from hallucination (20.8% vs. 12.1%) but worse from neglect (15.3% vs. 20.4%), suggesting neglect is often avoided upstream rather than repaired downstream.Source: G Visual Claim Accuracy Matters More… · p. 19
- Confidence calibration has +43.9 Lift when visual claims are accurate and +73.5 Lift when they are inaccurate.Visual grounding quality and metacognitive quality capture distinct aspects of successful VLM reasoning: calibration is especially informative under imperfect visual interpretation.Source: G Visual Claim Accuracy Matters More… · p. 19
- Converging checks accompany correct answers: traces verify solutions through substitution and logarithms or through multiple equivalent percentage calculations.In one example, the model says agreement between methods increases its confidence.Source: G Visual Claim Accuracy Matters More… · pp. 45–46
- Uncertainty acknowledgment is not sufficient for correctness: models resolve confusion to correct answers in some examples, but one direction-of-turn trace ends with an incorrect answer.The polar-coordinate trace rederives the angle algebraically, and the translation-mismatch trace checks the option against the passage; the skater trace still answers west when the ground truth is east.Source: G Visual Claim Accuracy Matters More… · pp. 48, 50
- Grounding reasoning in a definition or formal structure supports correct conclusions, as in identifying an unobservable internal state and matching a Modus Tollens argument.The behavior example applies the observability criterion to answer C; the logic example recognizes the structure and selects B.Source: G Visual Claim Accuracy Matters More… · pp. 49, 51
- Testing alternatives can help identify a valid solution: one trace rejects several AM-GM configurations before reaching the correct minimum of 16.The note describes four distinct configurations being ruled out before the model pivots to a substitution.Source: G Visual Claim Accuracy Matters More… · p. 51
- Reconsideration can leave reasoning unresolved: a trace produces conflicting sums, expresses confusion, and notes a possible earlier mistake without a final resolution in the supplied passage.The supplied alternatives give -50 and a separate grouping that yields a conflicting result.Source: G Visual Claim Accuracy Matters More… · p. 52
- −50 is the corrected final answer after the model recounts terms, finds a miscount, and repairs a derivation that had produced −51.The trace reports 49 inner pairs and computes 1 + 49 − 100 = −50 before confirming the ground-truth answer.Source: G Visual Claim Accuracy Matters More… · p. 52
- After reaching a wrong solution set, the model rechecks its algebra and identifies a mistaken constant term.The trace shows coefficient comparisons yielding a = 1/2 and c = −1, followed by the model noting that this differs from its previous solution.Source: G Visual Claim Accuracy Matters More… · p. 53