2505.11140

# Follow the Path: Reasoning over Knowledge Graph Paths to Improve LLM Factuality Abstract We introduce fs1, a simple yet effective method that improves the factuality of reasoning traces by sourcing them from large reasoning models (e.g., DeepSeek-R1) and grounding them by conditioning on knowledge graph (KG) paths. We fine-tune eight instruction-tuned Large Language Models (LLMs) on 3.9K factually grounded reasoning traces and rigorously evaluate them on six complex open-domain question-answering (QA) benchmarks encompassing 23.9K questions. Our results demonstrate that our fs1 -tuned model (32B parameters) consistently outperforms instruction-tuned counterparts with parallel sampling by 6-14 absolute points (pass@ $16$ ). Our detailed analysis shows that fs1 considerably improves model performance over more complex questions (requiring 3 or more hops on KG paths) and numerical answer types compared to the baselines. Furthermore, in single-pass inference, we notice that smaller LLMs show the most improvements. While prior works demonstrate the effectiveness of reasoning traces primarily in the STEM domains, our work shows strong evidence that anchoring reasoning to factual KG paths is a critical step in transforming LLMs for reliable knowledge-intensive tasks. 1 Introduction Factual consistency of LLM-generated output is a requirement for critical real-world applications. LLM reasoning in the form of “thinking” has shown promising improvements in model performance on complex downstream tasks, such as mathematical reasoning and puzzle-like questions using additional compute resources during inference (e.g., test-time scaling; Wu et al., 2024; Muennighoff et al., 2025; Zhang et al., 2025). However, it remains an open question whether these reasoning techniques improve factuality, particularly for complex multi-hop QA (mQA). This task tests a model’s ability to answer a question by synthesizing information from multiple pieces of evidence, often spread across different resources and requiring reasoning steps. We hypothesize that reasoning models should perform better than non-reasoning LLMs on the mQA task. To test this hypothesis, we source reasoning traces from state-of-the-art reasoning models and fine-tune several non-reasoning LLMs to attempt to induce reasoning capabilities. However, we have no guarantee that these reasoning traces from the large reasoning models are factually correct. In order to have a formal factual grounding in these traces, we condition the models on retrieved knowledge graph (KG) paths relevant to the questions. This is possible as KGs encode facts as directed, labeled graphs over entities and relations, which offers a verifiable foundation to inform each step of the reasoning process. We call our approach fs1 (factual simple test-time scaling; Muennighoff et al., 2025). We fine-tune eight different LLMs sizes on the original reasoning traces (rt; 3.4K samples) or on our KG-enhanced traces (fs1; 3.9K samples). We evaluate the fine-tuned models on six QA test sets spanning 23.9K questions, finding that fine-tuning on this amount of data can improve accuracy by 6-14 absolute points (pass@ $16$ ) for a 32B parameter model across the benchmarks. A snapshot of our method is in Figure 1. This setup enables us to address our research question (RQ): To what extent does grounding the reasoning processes of LLMs in KG paths enhance their factual accuracy for mQA? To address this question, our contributions are as follows: <details> <summary>x1.png Details</summary>  ### Visual Description ## Flowchart: Comparison of AI Reasoning Methods for Art History Question ### Overview The image presents a comparative analysis of four AI reasoning approaches to answer the question: "The visual artist that created the art series of Las Meninas, where did they live?" Each method is represented by a robot icon with a speech bubble containing its response, followed by a red X (incorrect) or green checkmark (correct). The correct answer is Madrid, as indicated by the final section. ### Components/Axes 1. **Methods Tested**: - Instruction-tuned - Chain-of-Thought - Thinking (rt) - Thinking with KGs (fs1) 2. **Result Indicators**: - Red X: Incorrect answer - Green checkmark: Correct answer 3. **Textual Content**: - Speech bubbles contain reasoning processes or final answers - Wikidata logo appears in the final method, indicating external knowledge integration ### Detailed Analysis 1. **Instruction-tuned**: - Response: "The answer is Paris." - Result: ❌ Incorrect 2. **Chain-of-Thought**: - Response: "Let me think step-by-step... My answer is Vienna." - Result: ❌ Incorrect 3. **Thinking (rt)**: - Response: ``` The answer should be Barcelona. ``` - Result: ❌ Incorrect 4. **Thinking with KGs (fs1)**: - Response: ``` <Question> + Wikidata The visual artist who created the art series [...] The answer is Madrid. ``` - Result: ✅ Correct ### Key Observations - **Accuracy Correlation**: Only the KGs-integrated method (fs1) provides the correct answer (Madrid), while others fail despite varying reasoning approaches. - **Knowledge Dependency**: The final method explicitly references Wikidata, suggesting external knowledge integration is critical for accuracy. - **Reasoning Flaws**: Non-KG methods exhibit either: - Direct incorrect answers (Instruction-tuned, Chain-of-Thought) - Incomplete reasoning loops (Thinking (rt)) ### Interpretation This diagram demonstrates the limitations of pure instruction tuning and chain-of-thought reasoning in knowledge-intensive tasks. The KGs-augmented approach (fs1) achieves accuracy by: 1. Structuring queries for knowledge retrieval (`<Question> + Wikidata`) 2. Maintaining reasoning context (` </details> Figure 1: Snapshot of Method. We show a snapshot of the experiments executed in this study. There are four settings on how a question can be answered; (1) direct answer from an instruction-tuned model, (2) step-by-step reasoning via Chain-of-Thought, (3) original “thinking”, and (4) knowledge-graph enhanced “thinking”. We show an example of how (4) looks like in Figure 4. - We demonstrate that with test-time scaling (parallel sampling), our fs1 -tuned Qwen2.5-32B model improves factual accuracy by 6-14 absolute points (at pass@ $16$ ). - We conduct an analysis over the question and answer types (e.g., question difficulty, answer type, and domains) to investigate where fs1 -tuned models provide improvements. We find that fs1 -tuned models perform better on more difficult questions, requiring 3 hops or more. - We examine performance of eight fs1 -tuned models (360M-32B parameters) in a pass@ $1$ setting against baselines. We find that smaller LLMs have the largest increase in performance, whereas larger models see less profound improvements. - We release 3.4K raw reasoning traces and 3.9K KG-enhanced reasoning traces both sourced from QwQ-32B and Deepseek-R1. All code, datasets, and models are publicly available under an MIT license: https://github.com/jjzha/fs1. 2 Reasoning Data rt: Distilling Reasoning Traces. To obtain reasoning traces, we use ComplexWebQuestions (CWQ; Talmor & Berant, 2018), a dataset designed for complex mQA. The CWQ dataset is created by automatically generating complex SPARQL queries based on Freebase (Bollacker et al., 2008). These queries are then automatically transformed into natural language questions, which are further refined by human paraphrasing. We take the CWQ dev. set, which consists of 3,519 questions, to curate the reasoning traces. We query both QwQ-32B (Qwen Team, 2025) and Deepseek-R1 (671B; DeepSeek-AI, 2025). By querying the model directly with a question, e.g., “ What art movement was Pablo Picasso part of? ”, we retrieve the reasoning traces surrounded by “think” tokens (<think>...</think>) and force the model to give the final answer to the question in \boxed{} format. We extract around 3.4K correct-only traces (final answer is correct), which we call rt. We show full examples in Figure 8 and Figure 9 (Appendix C). fs1: Enhancing Reasoning Traces with Knowledge Graph Paths. We attempt to steer the reasoning traces with KG paths to remove the inaccuracies in the traces. Since the CWQ dataset consists of entities from Freebase, we align them to their corresponding Wikidata entities. For each question in the dev. set of the CWQ dataset, relevant KG paths are extracted from Wikidata using random walks using SPARQL queries as shown in Appendix E. Each mQA pair in the dataset may contain multiple valid KG paths, which are linearized graphs that retain the structural information of the KG. The paths are generated by extracting the relevant entities from the question and the gold answer. These diverse KG paths that can lead to the same answer reflect the possible diversity of the reasoning traces. Therefore, including linearized graphs improves the interpretability and the explainability of the reasoning traces. The prompt to obtain the improved reasoning traces is shown in Figure 4, by prompting QwQ-32B and Deepseek-R1 again. Full examples are in Figure 10 and Figure 11 (Appendix C). Table 1: Training Data Statistics. Statistics of reasoning traces of QwQ-32B and Deepseek-R1 on CWQ based on the Qwen2.5-32B tokenizer. The original reasoning traces (rt) are from simply querying the question to the reasoning models, whereas fs1 indicates the statistics when queried with the knowledge graphs. We calculate the performance of the models’ final answer via LLM-as-a-Judge. We show that fs1 has higher performance in terms of accuracy compared to rt. | | | QwQ-32B | R1-685B | Total | | | | | --- | --- | --- | --- | --- | --- | --- | --- | | rt | fs1 | rt | fs1 | rt | fs1 | | | | Exact Match | | 0.46 | $\uparrow$ 0.63 | 0.56 | $\uparrow$ 0.72 | 0.51 | $\uparrow$ 0.67 | | Sem. Match (all-MiniLM-L6-v2) | | 0.50 | $\uparrow$ 0.58 | 0.55 | $\uparrow$ 0.63 | 0.52 | $\uparrow$ 0.60 | | LLM-as-a-Judge (gpt-4o-mini) | | 0.44 | $\uparrow$ 0.61 | 0.54 | $\uparrow$ 0.70 | 0.49 | $\uparrow$ 0.65 | | Samples with only correct answers | | | | | | | | | Number of Samples | | 1,533 | 1,972 | 1,901 | 1,914 | 3,434 | 3,886 | | Avg. Reasoning Length (subwords) | | 937 | 897 | 1,043 | 637 | 990 | 767 | | Avg. Answer Length (subwords) | | 40 | 93 | 64 | 116 | 52 | 104 | <details> <summary>x2.png Details</summary>  ### Visual Description ## Violin Plot: Token Count Distribution by Data Source and Model ### Overview The image presents a dual-panel violin plot comparing token count distributions across two data sources (`rt` and `fs1`) for two AI models: **QwQ-32B** (blue) and **DeepSeek-R1** (orange). Each violin plot visualizes the distribution of subword tokens, with medians and quartile ranges annotated. --- ### Components/Axes - **X-Axis (Data Source)**: - Categories: `rt` (left) and `fs1` (right). - **Y-Axis (Token Count)**: - Scale: 0 to 8,000 subwords. - **Legends**: - **Left Panel (QwQ-32B)**: Blue color. - **Right Panel (DeepSeek-R1)**: Orange color. - **Annotations**: - Median lines (solid horizontal lines). - Quartile ranges (dashed horizontal lines labeled `Q1` and `Q3`). --- ### Detailed Analysis #### QwQ-32B (Blue) - **`rt`**: - Median: ~552 subwords. - Q1: ~392 subwords. - Q3: ~1,017 subwords. - Distribution: Sharp peak at median, with a long tail extending to ~8,000 subwords (outlier). - **`fs1`**: - Median: ~553 subwords. - Q1: ~386 subwords. - Q3: ~1,039 subwords. - Distribution: Symmetric, with a narrower spread than `rt`. #### DeepSeek-R1 (Orange) - **`rt`**: - Median: ~635 subwords. - Q1: ~431 subwords. - Q3: ~1,274 subwords. - Distribution: Higher median than QwQ-32B, with a pronounced outlier at ~6,000 subwords. - **`fs1`**: - Median: ~496 subwords. - Q1: ~359 subwords. - Q3: ~792 subwords. - Distribution: Narrower and more symmetric than `rt`. --- ### Key Observations 1. **Model-Specific Trends**: - **QwQ-32B** shows near-identical medians for `rt` and `fs1` (~552 vs. ~553), suggesting consistent performance across data sources. - **DeepSeek-R1** has a significantly higher median for `rt` (~635) compared to `fs1` (~496), indicating data-source-dependent behavior. 2. **Outliers**: - Both models exhibit extreme values in `rt` (QwQ-32B: ~8,000; DeepSeek-R1: ~6,000), suggesting rare high-token-count events. 3. **Quartile Spread**: - QwQ-32B’s `rt` has a wider interquartile range (Q1–Q3: ~625 subwords) compared to DeepSeek-R1’s `rt` (~843 subwords), implying greater variability in QwQ-32B’s `rt` data. --- ### Interpretation - **Data Source Impact**: - DeepSeek-R1 processes more tokens on average for `rt` data, while QwQ-32B shows minimal difference between `rt` and `fs1`. - **Efficiency Insights**: - The lower median for DeepSeek-R1’s `fs1` (~496) suggests it may handle `fs1` data more efficiently, though with less variability. - **Outlier Implications**: - The extreme values in `rt` for both models could indicate anomalies or specialized use cases requiring high token counts (e.g., long-form text processing). The data highlights model-specific strengths: QwQ-32B demonstrates consistency, while DeepSeek-R1 excels in `rt` token handling but shows greater variability. </details> Figure 2: Distribution of Reasoning Traces. We show the distribution of the reasoning length among the queried models. In the left plot, we show rt and in the right plot, we show fs1. We show that particularly for fs1 and Deepseek-R1, the reasoning length is shorter in terms of subwords. Data Statistics. In Table 1 and Figure 2, we compare the reasoning trace accuracy and statistics of rt and fs1. We evaluate reasoning traces using three methods: (1) Exact Match, checking if the \boxed{} answer exactly matches or is a sub-phrase of any gold answer; (2) Semantic Match, accepting answers with a cosine similarity score $>$ 0.5; and (3) LLM-as-a-Judge, verifying entity alignment using gpt-4o-mini-2024-07-18. Results show that fs1 achieves higher accuracy, indicating that it contains more factual answers. Traces from rt are longer (up to 1K subwords), fs1 traces are typically shorter (around 800 subwords). The median length in subwords is similar for QwQ-32B (552 for rt and 553 for fs1), while there is a difference for Deepseek-R1 (635 median for rt and 496 for fs1). Spot-checking reveals that fs1 yields a more definitive answer. 3 Methodology 3.1 Training and Inference We fine-tune six Qwen2.5-Instruct models (0.5B to 32B) on rt and fs1, using only reasoning traces with correct final answers. During inference, we evaluate the model on the original questions to test its performance. Following Muennighoff et al. (2025), we train for 5 epochs with a sequence length of 8,192, a batch size of 16, a learning rate of $1× 10^{-5}$ (cosine schedule, 5% warmup), and a weight decay of $1× 10^{-4}$ . The models are optimized with a standard supervised fine-tuning (SFT) loss, which minimizes the negative log-likelihood (implemented as the cross-entropy function) of target tokens in an autoregressive manner. Let $y_{t}^{*}$ be the correct token and $p_{\theta}(y_{t}^{*}\mid x,y_{<t})$ be the model’s probability of predicting it. The model optimizes the function: $$ \mathcal{L}_{\text{SFT}}(\theta)=-\frac{1}{T}\sum_{t=1}^{T}\log p_{\theta}\left(y_{t}^{*}\mid x,y_{<t}\right). \tag{1} $$ For inference, we use a temperature ( $T$ ) of $0.7$ and top_p of $0.8$ for original instruct models. Otherwise, we use $T=0.6$ and top_p of $0.95$ . Further details on hardware and costs are in Appendix B. fs1 Prompt Example ⬇ When did the sports team owned by Leslie Alexander win the NBA championship? While answering the question, make use of the following linearised graph as an inspiration in your reasoning, not as the only answer: 1994 NBA Finals, winner, Houston Rockets Houston Rockets, owned by, Leslie Alexander 1995 NBA Finals, winner, Houston Rockets Houston Rockets, owned by, Leslie Alexander. Put your final answer within \ boxed {}. – (For illustration) Gold Answer: [”1994 and 1995”] Figure 3: fs1 Prompt Example. We depict how we prompt both Deepseek-R1 and QwQ-32B to obtain better reasoning traces with KG paths. LLM-as-a-Judge (Llama-3.3-70B) ⬇ gold answer: [” Joule per gram per kelvin ”, ” Joule per kilogram per kelvin ”] predicted answer: ” J /(kg $ \ cdot$ K)” Is the gold answer entity or value contained in the predicted answer? Respond only with 0 (no) or 1 (yes). # Llama -3.3-70 B - Instruct outputs ”1” Figure 4: Prompt for LLM-as-a-Judge. We show the LLM-as-a-Judge prompt for evaluating whether the predicted and gold answer refer to the same real-world entity, where regular exact string matching will not capture the alignment between the gold and predicted answer in this example (i.e., the measurement unit). Table 2: Test Benchmark. Overview of the mQA test sets used in our evaluation. | CWQ (Talmor & Berant, 2018) | apache-2.0 | 3.5K | Multi-hop QA from WebQuestionsSP with compositional SPARQL queries for Freebase paraphrased by crowd workers. | | --- | --- | --- | --- | | ExaQT (Jia et al., 2021) | cc-by-4.0 | 3.2K | Temporal-QA benchmark combining eight KB-QA datasets, focusing on time-specific queries. | | GrailQA (Gu et al., 2021) | apache-2.0 | 6.8K | Freebase QA dataset with annotated answers and logical forms (SPARQL/S-expressions) across 86 domains. | | SimpleQA (Wei et al., 2024a) | MIT | 4.3K | Fact-seeking questions with verified answers, designed to measure and challenge the factual accuracy of language models. | | Mintaka (Sen et al., 2022) | cc-by-4.0 | 4.0K | Multilingual QA (9 languages), entity-linked pairs across diverse domains (English test split). | | WebQSP (Yih et al., 2016) | apache-2.0 | 2.0K | Enhanced WebQuestions with Freebase QA annotated with SPARQL ( $\sim$ 82% coverage). | | Total | | 23.9K | | <details> <summary>x3.png Details</summary>  ### Visual Description ## Heatmaps: Model Comparison Metrics ### Overview The image presents three comparative heatmaps analyzing question-answering models (CWQ_train, CWQ_test, ExaQT, GrailQA, SimpleQA, Mintaka, WebQSP) across three metrics: 1. **Cosine Similarity Count (>0.90)** 2. **Exact Match Count** 3. **Average Cosine Similarity** Values are color-coded (yellow=highest, green=medium, dark green=lowest) and positioned in a matrix format. --- ### Components/Axes - **X-axis (Columns)**: Models compared as reference points (CWQ_train, CWQ_test, ExaQT, GrailQA, SimpleQA, Mintaka, WebQSP). - **Y-axis (Rows)**: Models being evaluated against the reference points. - **Legend**: - **Yellow**: Highest values (e.g., 109, 0.15). - **Green**: Medium values (e.g., 80, 0.12). - **Dark Green**: Lowest values (e.g., 0, 0.05). - **Placement**: Legend is positioned to the right of each heatmap. --- ### Detailed Analysis #### 1. **Cosine Similarity Count (>0.90)** - **CWQ_train vs CWQ_test**: 109 (yellow). - **ExaQT vs CWQ_train**: 51 (green), **ExaQT vs CWQ_test**: 80 (green). - **GrailQA**: All values ≤1 (dark green). - **SimpleQA**: 1 match (dark green) in SimpleQA vs Mintaka. - **Mintaka**: 4 matches (green) in Mintaka vs ExaQT. - **WebQSP**: 12 (green) vs CWQ_train, 26 (green) vs CWQ_test, 83 (yellow) vs GrailQA, 15 (dark green) vs WebQSP. #### 2. **Exact Match Count** - **All values are 0** except: - **WebQSP vs CWQ_test**: 1 (yellow). #### 3. **Average Cosine Similarity** - **CWQ_train vs CWQ_test**: 0.15 (yellow). - **ExaQT**: 0.11 (vs CWQ_train), 0.12 (vs CWQ_test). - **GrailQA**: 0.08 (vs CWQ_train/CWQ_test), 0.05 (self-comparison). - **SimpleQA**: 0.11–0.13 (vs CWQ_train/CWQ_test), 0.07 (self-comparison). - **Mintaka**: 0.11–0.12 (vs CWQ_train/CWQ_test), 0.07 (vs SimpleQA), 0.11 (self-comparison). - **WebQSP**: 0.12–0.13 (vs CWQ_train/CWQ_test), 0.06 (vs GrailQA), 0.11 (self-comparison). --- ### Key Observations 1. **Dominance of CWQ_train/CWQ_test**: - Highest Cosine Similarity Count (109) and Average Similarity (0.15) between CWQ_train and CWQ_test. - Suggests these models share significant structural or data overlap. 2. **ExaQT Performance**: - Strong similarity to CWQ_train/test (51–80 matches, 0.11–0.12 average). - Indicates potential alignment with CWQ frameworks. 3. **Sparse Exact Matches**: - Only 1 exact match (WebQSP vs CWQ_test) across all models. - Highlights rarity of identical outputs despite similarity. 4. **Model-Specific Trends**: - **GrailQA/SimpleQA**: Low similarity (≤0.08 average), suggesting divergent approaches. - **WebQSP**: Moderate similarity (0.12–0.13) but low exact matches, implying nuanced differences. --- ### Interpretation - **CWQ Framework Centrality**: CWQ_train/test act as a hub, with ExaQT showing partial alignment. Other models (GrailQA, SimpleQA) diverge significantly, possibly due to architectural or training differences. - **Similarity ≠ Exactness**: High cosine similarity (e.g., WebQSP vs CWQ_train: 0.12) does not guarantee exact matches, suggesting semantic vs. lexical differences. - **Outliers**: - **SimpleQA vs Mintaka**: 4 matches (green) despite low average similarity (0.07), indicating sporadic overlaps. - **WebQSP Self-Comparison**: 0.11 average similarity but 0 exact matches, highlighting internal variability. - **Implications**: - CWQ and ExaQT may share foundational design principles. - GrailQA/SimpleQA require further investigation to identify unique methodologies. - WebQSP’s moderate similarity but low exactness suggests potential for refinement in alignment strategies. </details> Figure 5: Data Overlap. We show data overlap between the train set and benchmark. On the left, one can observe the count of similar questions when the cosine similarity $>$ 0.90 (measured with paraphrase-MiniLM-L6-v2; Reimers & Gurevych, 2019). In the middle, we measure exact match counts. On the right, we show the average pairwise cosine similarity across the full test sets. 3.2 Benchmarks and Evaluation We show the test datasets, licenses, size and a short description in Table 2. We have four baselines, namely Qwen2.5-72B-Instruct (Qwen Team, 2024), QwQ-32B, Deepseek-R1, and o3-mini (OpenAI, 2025). To evaluate our models, we select a suite of six mQA benchmarks with a total of 23.9K questions. We have four setups for benchmarking the models: (1) All models including baselines are evaluated zero-shot (i.e., only querying the question); (2) the models are queried using zero-shot chain-of-thought prompting (Kojima et al., 2022; Wei et al., 2022), where we simply append the prompt “ Put your final answer within \boxed{}. Think step-by-step. ”; (3) we benchmark the models fine-tuned on rt; (4) we benchmark the models fine-tuned on fs1. In Figure 12 (Section D.1), we show an example of each dataset in the test benchmark. Possible Data Leakage. In Figure 5, we show the overlap of the questions in the training set of ComplexWebQuestions (CWQ_train) versus all the other benchmarks used in our study (all questions lower-cased). On the left, we count the times that the cosine similarity between questions exceeds 0.90. We can see that there is the most overlap between CWQ_train and CWQ_test (109 questions), and the second most is between WebQSP and ExaQT (83 questions). In the middle, we show that there is almost to none exact string match between the questions. On the right, we show the average pairwise cosine similarity across the benchmarks is lower or equal to 0.15. Evaluation Metric. Similar to previous studies, e.g., Ma et al. (2025), we report pass@ $k$ , which reflects the probability that at least one out of $k$ randomly selected completions (drawn from a total of $n$ completions per problem) is correct. As such, it serves as an upper-bound on practical performance, which would require a subsequent selection mechanism. Formally, pass@ $k$ is given by: $\mathbb{E}_{\text{problems}}\left[1-\frac{\binom{n-c}{k}}{\binom{n}{k}}\right]$ , where $n$ is the number of generated completions per problem and $c$ is the count of correct completions (Chen et al., 2021). For our benchmarks, we evaluate $k=\{1,2,4,8,16\}$ . In practice, pass@32 is typically reported for formal theorem-proving tasks, while pass@1 (reducing to standard top-1 accuracy) is standard for math and coding tasks as mentioned by Ma et al. (2025). In this work for factual mQA, we report until $k=16$ . LLM-as-a-Judge. To decide whether an answer is correct or not (1 or 0), our main evaluation approach is using LLM-as-a-judge with Llama-3.3-70B-Instruct We compare both gpt-4o-mini-2024-07-18 and Llama-3.3-70B-Instruct on a large subsample of our outputs and saw there there is almost no difference in predictions. Additionally, Llama-3.3-70B is rated higher in LM Arena than gpt-4o-mini (at time of writing 79 ${}^{\text{th}}$ vs. 83 ${}^{\text{rd}}$ respectively). to determine whether a predicted answer obtained from the \boxed{} output is referring to the same real-world entity as the gold answer. An example of this is shown in Figure 4. When the model does not generate a \boxed{} output, we take the last 10 subwords as predicted answer, which LLM-as-a-judge can infer what the predicted real-world entity is when there is not exact string matching. This same approach is used in Table 1. Compared to exact string matching and semantic similarity evaluation methods, LLM-as-a-Judge rates the quality of output similarly compared to the other methods. <details> <summary>x4.png Details</summary>  ### Visual Description ## Line Graphs: Performance Comparison Across Datasets ### Overview The image contains six line graphs arranged in two rows and three columns, comparing the performance of four methods (`inst`, `cot`, `rt`, `fs1`) across six datasets (`ComplexWebQuestions`, `ExaQT`, `GrailQA`, `Mintaka`, `SimpleQA`, `WebQSP`). Each graph plots `pass@k (%)` on the y-axis against `k` (1–16) on the x-axis. The legend at the bottom center identifies the methods with distinct colors and markers: `inst` (blue line), `cot` (green dashed line), `rt` (purple dotted line), and `fs1` (red square markers). --- ### Components/Axes - **Y-axis**: `pass@k (%)` (performance metric, ranging from ~35% to 85% depending on the dataset). - **X-axis**: `k` (integer values from 1 to 16, likely representing a parameter like sample size or iteration count). - **Legend**: Located at the bottom center, with four entries: - `inst`: Solid blue line. - `cot`: Green dashed line. - `rt`: Purple dotted line. - `fs1`: Red square markers. - **Graph Titles**: Each subplot is labeled with a dataset name (e.g., `ComplexWebQuestions`, `Mintaka`). --- ### Detailed Analysis #### ComplexWebQuestions - **Trends**: All methods show upward trends. `fs1` (red) starts highest (~55% at k=1) and increases steeply to ~68% at k=16. `inst` (blue) starts lowest (~45% at k=1) and rises to ~53% at k=16. `cot` (green) and `rt` (purple) follow intermediate trajectories (~50–63%). - **Key Data Points**: - k=1: `fs1` ~55%, `inst` ~45%, `cot` ~48%, `rt` ~52%. - k=16: `fs1` ~68%, `inst` ~53%, `cot` ~58%, `rt` ~63%. #### ExaQT - **Trends**: Similar upward trajectories. `fs1` leads (~45% at k=1 → ~55% at k=16). `inst` (~38% → ~47%), `cot` (~42% → ~52%), and `rt` (~40% → ~53%) lag slightly behind. - **Key Data Points**: - k=1: `fs1` ~45%, `inst` ~38%, `cot` ~42%, `rt` ~40%. - k=16: `fs1` ~55%, `inst` ~47%, `cot` ~52%, `rt` ~53%. #### GrailQA - **Trends**: `fs1` dominates (~35% → ~55%). `inst` (~30% → ~45%), `cot` (~32% → ~48%), and `rt` (~34% → ~51%) show moderate gains. - **Key Data Points**: - k=1: `fs1` ~35%, `inst` ~30%, `cot` ~32%, `rt` ~34%. - k=16: `fs1` ~55%, `inst` ~45%, `cot` ~48%, `rt` ~51%. #### Mintaka - **Trends**: Highest performance overall. `fs1` (~70% → ~85%), `inst` (~65% → ~78%), `cot` (~72% → ~82%), and `rt` (~70% → ~83%) all improve significantly. - **Key Data Points**: - k=1: `fs1` ~70%, `inst` ~65%, `cot` ~72%, `rt` ~70%. - k=16: `fs1` ~85%, `inst` ~78%, `cot` ~82%, `rt` ~83%. #### SimpleQA - **Trends**: `fs1` (~10% → ~22%), `inst` (~12% → ~18%), `cot` (~15% → ~21%), and `rt` (~13% → ~20%) show steep gains. - **Key Data Points**: - k=1: `fs1` ~10%, `inst` ~12%, `cot` ~15%, `rt` ~13%. - k=16: `fs1` ~22%, `inst` ~18%, `cot` ~21%, `rt` ~20%. #### WebQSP - **Trends**: `fs1` (~60% → ~78%), `inst` (~55% → ~70%), `cot` (~62% → ~74%), and `rt` (~60% → ~75%) improve consistently. - **Key Data Points**: - k=1: `fs1` ~60%, `inst` ~55%, `cot` ~62%, `rt` ~60%. - k=16: `fs1` ~78%, `inst` ~70%, `cot` ~74%, `rt` ~75%. --- ### Key Observations 1. **Consistent Ranking**: `fs1` (red) consistently outperforms other methods across all datasets, followed by `cot` (green), `rt` (purple), and `inst` (blue). 2. **Dataset Variability**: Performance levels vary by dataset (e.g., `Mintaka` has the highest baseline performance, while `SimpleQA` starts lowest). 3. **Scaling with k**: All methods improve as `k` increases, suggesting `k` represents a parameter (e.g., sample size) that enhances performance when increased. 4. **Convergence**: In some datasets (e.g., `Mintaka`), the gap between methods narrows at higher `k` values. --- ### Interpretation The data demonstrates that `fs1` is the most effective method across all datasets, likely due to its architectural or algorithmic advantages. The consistent upward trend for all methods with increasing `k` implies that larger values of `k` (e.g., more training data or iterations) improve performance universally. Dataset-specific differences (e.g., `Mintaka` vs. `SimpleQA`) may reflect variations in complexity or task difficulty. The legend’s spatial placement (bottom center) ensures clarity, while the uniform axis scaling across subplots allows direct comparison. No outliers or anomalies are observed, reinforcing the reliability of the trends. </details> Figure 6: Upper-bound Test-Time Scaling for Factual Reasoning. We show with Qwen2.5-32B that parallel scaling is beneficial for complex mQA, measured by pass@ $k$ , especially when fine-tuned on fs1, instead of conducting single-pass inference. 4 Results and Discussion 4.1 Results with Test-time Scaling Parallel scaling can achieve lower latency by enabling multiple (identical) models to run simultaneously locally (via e.g., multiple GPUs or batching techniques) or via API based methods to generate multiple answers. Formally, parallel sampling entails an aggregation technique that combines $N$ independent solutions into a single final prediction, commonly known as a best-of- $N$ approach (Chollet, 2019; Irvine et al., 2023; Brown et al., 2024a; Li et al., 2022). Formally, given a set of $N$ predictions $P=\{p_{1},...,p_{N}\}$ , the best-of- $N$ method selects a prediction $p∈ P$ as the final output. In this work, we present results using pass@ $k$ (see Section 3.2), extending the number of sampled $k$ (until $k$ = 16). In Figure 6, we show parallel scaling results by performing 16 inference runs with Qwen2.5-32B -Instruct, CoT, rt, fs1 on each test dataset. For parallel sampling, we limit ourselves to Qwen2.5-32B as running 16 inferences for 8 models for all 4 settings would require 12.2M model inferences for the test benchmarks, which is computationally prohibitive. As $k$ increases, pass@ $k$ (indicating whether at least one generation is correct) rises steadily across all benchmarks. Parallel sampling boosts the chance of producing a correct answer, especially when fine-tuned on fs1. For example, on CWQ, we see a performance increase of 16 absolute points at $k$ = 16 and on SimpleQA around 6 absolute points at the same $k$ compared to their original instruction-tuned counterpart. <details> <summary>x5.png Details</summary>  ### Visual Description ## Bar Chart: Relative Improvement (RI) by Hops ### Overview The chart visualizes the Relative Improvement (RI) percentage across four hop categories (1, 2, 3, 3+) for three metrics: `cot`, `rt`, and `fs1`. RI is measured against a baseline (`pass@16`), with values ranging from 0% to 60%. Each metric is represented by a distinct color-coded bar group per hop category. ### Components/Axes - **X-axis**: Hop categories labeled as "1", "2", "3", and "3+". - **Y-axis**: RI (%) with a secondary label "pass@16" in parentheses, scaled from 0 to 60. - **Legend**: Located on the right, mapping: - `cot` → light blue (solid) - `rt` → medium blue (diagonal stripes) - `fs1` → dark blue (diagonal stripes) - **Bar Groups**: Each hop category contains three adjacent bars (one per metric), ordered left-to-right as `cot`, `rt`, `fs1`. ### Detailed Analysis #### Hop Category 1 - `cot`: ~38% (light blue) - `rt`: ~36% (medium blue) - `fs1`: ~32% (dark blue) #### Hop Category 2 - `cot`: ~39% (light blue) - `rt`: ~45% (medium blue) - `fs1`: ~34% (dark blue) #### Hop Category 3 - `cot`: ~33% (light blue) - `rt`: ~52% (medium blue) - `fs1`: ~60% (dark blue) #### Hop Category 3+ - `cot`: ~33% (light blue) - `rt`: ~24% (medium blue) - `fs1`: ~50% (dark blue) ### Key Observations 1. **`fs1` Dominance in High Hops**: `fs1` achieves the highest RI in categories 3 (60%) and 3+ (50%), suggesting superior performance in complex scenarios. 2. **`rt` Volatility**: `rt` peaks at 52% in category 3 but drops sharply to 24% in 3+, indicating potential instability in extreme cases. 3. **`cot` Stability**: `cot` remains relatively flat (~33–39%) across all categories, acting as a consistent baseline. 4. **Divergence in 3+**: The largest gap between metrics occurs in 3+, where `fs1` (50%) outperforms `rt` (24%) by 26 percentage points. ### Interpretation The data implies that `fs1` is the most effective metric for improving RI in high-hop scenarios (3+), while `rt` underperforms in extreme cases. `cot` serves as a stable reference point, possibly representing a control or foundational measurement. The sharp decline in `rt` for 3+ suggests it may not scale well with increased complexity, whereas `fs1` maintains robustness. This could inform prioritization of `fs1` in systems requiring high-hop efficiency. </details> (a) Performance by number of hops required to answer the question measured in pass@ $16$ . <details> <summary>x6.png Details</summary>  ### Visual Description ## Bar Chart: Relative Improvement (RI) by Answer Type ### Overview The chart visualizes the Relative Improvement (RI) percentage for three answer types (`cot`, `rt`, `fs1`) across five categories (`date`, `place`, `person`, `other`, `number`). The y-axis represents RI (%) relative to a baseline (`pass@16`), while the x-axis categorizes answer types. The legend in the top-right corner maps colors to answer types: yellow (`cot`), teal (`rt`), and blue with diagonal stripes (`fs1`). ### Components/Axes - **X-axis**: Categories (`date`, `place`, `person`, `other`, `number`). - **Y-axis**: RI (%) relative to `pass@16`, ranging from 0 to 55%. - **Legend**: Located in the top-right corner, associating colors with answer types: - Yellow: `cot` - Teal: `rt` - Blue (diagonal stripes): `fs1` ### Detailed Analysis 1. **Date**: - `cot`: ~48% RI - `rt`: ~42% RI - `fs1`: ~55% RI (highest among all categories) 2. **Place**: - `cot`: ~32% RI - `rt`: ~20% RI - `fs1`: ~19% RI (lowest in this category) 3. **Person**: - `cot`: ~24% RI - `rt`: ~26% RI - `fs1`: ~13% RI (lowest RI for `fs1` across all categories) 4. **Other**: - `cot`: ~19% RI - `rt`: ~30% RI - `fs1`: ~31% RI (highest RI for `fs1` in this category) 5. **Number**: - `cot`: ~17% RI - `rt`: ~27% RI - `fs1`: ~30% RI ### Key Observations - **`fs1` dominates in `date` and `other` categories**, achieving the highest RI (~55% and ~31%, respectively). - **`cot` performs best in `place`** (~32% RI) but declines sharply in `person` and `number`. - **`rt` shows moderate consistency**, with RI ranging from 20% (`place`) to 30% (`other`). - **`fs1` underperforms in `person`** (~13% RI), significantly lower than other answer types in this category. - **`cot` has the steepest decline** from `date` (~48%) to `number` (~17%). ### Interpretation The data suggests that answer type effectiveness varies by category. `fs1` excels in `date` and `other` contexts, possibly due to its structural or contextual adaptability. `cot` performs well in `place` but struggles in `person` and `number`, indicating potential limitations in handling personal or numerical data. The drop in `fs1` RI for `person` (~13%) may reflect challenges in processing personal information. Overall, the chart highlights the importance of answer type selection based on the query category for optimizing RI. </details> (b) Performance by answer type (i.e., what type of entity the answer is) in pass@ $16$ . <details> <summary>x7.png Details</summary>  ### Visual Description ## Bar Chart: Relative Improvement (RI) by Domain ### Overview The chart visualizes Relative Improvement (RI) percentages across 11 domains for three data series: "cot" (blue), "rt" (purple), and "fs1" (red). RI is measured as a percentage of "pass@16" performance, with values ranging from 0% to 80% on the y-axis. Domains include art, sports, other, geography, tv shows, video games, politics, music, sci & tech, and history. ### Components/Axes - **X-axis (Domains)**: Art, sports, other, geography, tv shows, video games, politics, music, sci & tech, history. - **Y-axis (RI %)**: 0–80% scale with 20% increments. - **Legend**: - Blue (cot) - Purple (rt) - Red (fs1) - **Bar Style**: Grouped bars per domain, with diagonal hatching for rt and solid colors for cot/fs1. ### Detailed Analysis 1. **Art**: - cot: ~60% (blue) - rt: ~70% (purple) - fs1: ~55% (red) 2. **Sports**: - cot: ~45% (blue) - rt: ~35% (purple) - fs1: ~45% (red) 3. **Other**: - cot: ~40% (blue) - rt: ~30% (purple) - fs1: ~45% (red) 4. **Geography**: - cot: ~35% (blue) - rt: ~20% (purple) - fs1: ~45% (red) 5. **TV Shows**: - cot: ~30% (blue) - rt: ~50% (purple) - fs1: ~40% (red) 6. **Video Games**: - cot: ~30% (blue) - rt: ~60% (purple) - fs1: ~85% (red) 7. **Politics**: - cot: ~30% (blue) - rt: ~25% (purple) - fs1: ~40% (red) 8. **Music**: - cot: ~25% (blue) - rt: ~20% (purple) - fs1: ~25% (red) 9. **Sci & Tech**: - cot: ~15% (blue) - rt: ~15% (purple) - fs1: ~10% (red) 10. **History**: - cot: ~5% (blue) - rt: ~15% (purple) - fs1: ~15% (red) ### Key Observations - **fs1 Dominance**: fs1 consistently shows the highest RI in most domains (e.g., video games: 85%, art: 55%). - **rt Peaks**: rt outperforms others in art (70%) and video games (60%). - **cot Weakness**: cot has the lowest RI in history (5%) and sci & tech (15%). - **Anomalies**: - rt underperforms in geography (20%) and music (20%). - fs1 drops sharply in sci & tech (10%). ### Interpretation The data suggests **fs1** is the most effective domain-specific intervention overall, particularly in high-impact areas like video games. **rt** excels in creative domains (art, video games) but struggles in technical fields (geography, sci & tech). **cot** performs poorly in specialized domains (history, sci & tech), indicating potential limitations in adaptability. The stark contrast in fs1’s performance across domains (e.g., 85% in video games vs. 10% in sci & tech) highlights its domain-dependent efficacy. The low RI in sci & tech for all series may reflect inherent challenges in that domain or data collection biases. </details> (c) Performance measured per domain in pass@ $16$ . Figure 7: Relative Improvements across Different Axes. We show the relative performance improvement (%) at pass@ $16$ of different Qwen-32B (i.e., CoT, rt and fs1) against the original instruct model. In (a), we show the performance of the models by the number of hops required to answer the question. In (b), we show the performance of the models by answer type. In (c), we show the performance by domain of the question. Absolute numbers are in Figure 13 (Appendix F). 4.2 What type of samples do models seem to fail on? In Figure 7, we investigate what kind of questions the model (Qwen2.5-32B) seems to fail on. We take metadata information from SimpleQA (Wei et al., 2024a), which indicates the question difficulty in number of hops required to answer the question (Figure 7(a)), type of answer (Figure 7(b)) and the domain of the question (Figure 7(c)). For question difficulty, we source the number of hops for each question in SimpleQA from Lavrinovics et al. (2025). We count the number of relations (P) from Wikidata, which would indicate the number of hops required to go from entity A to B. When a question does not contain any relations, we assume it takes more than 3 hops to answer the question. In Figure 7(a), we observe that fs1 has lower relative improvements on easier questions (e.g., 1 or 2 hops required), but outperforms the other models when the question gets more complex (3 or more hops required). This indicates that inducing KG paths helps answering complex questions. In Figure 7(b), we show that fs1 has the most relative improvement on numerical answers, such as numbers, dates and also miscellaneous answer types. Last, in Figure 7(c), fs1 performs best on questions related to video games, geography, politics, music, and miscellaneous questions. Additionally, rt performs best on art and history-related questions. Last, CoT performs best on questions related to sports. 4.3 Single pass results across scale In Table 3, we show results in terms of accuracy via LLM-as-a-Judge at pass@ $1$ (i.e., one inference run per question) on all test datasets. For the baselines, we observe that o3-mini is the dominant model, achieving the highest score on five out of six datasets, such as its 0.774 accuracy on Mintaka and 0.680 on WebQSP. The only exception is SimpleQA, where R1-70B performs best with a score of 0.188. These are followed by Qwen2.5-72B-Instruct and QwQ-32B in overall performance. Observing the Qwen2.5 results, the benefits of fine-tuning on rt and fs1 are most pronounced at the sub-billion parameter scale. For instance, fine-tuning the 0.5B model on fs1 yields substantial relative gains across all tasks, peaking at a +74.6% on WebQSP. However, as model size increases, the performance differences become more nuanced. For the 1.5B model, the same fs1 fine-tuning leads to performance degradation on four out of six datasets, such as ExaQT (-4.7%) and WebQSP (-1.1%). While larger models like the 32B still benefit from fine-tuning (e.g., rt and fs1 are often the best performers in their group), the relative gains are smaller than those seen at the 0.5B scale. Our results also show that fine-tuning improvements do not uniformly generalize across different model families at the sub-billion parameter scale. A comparison between the fine-tuned Qwen2.5 and SmolLM2 models reveals a significant performance divergence. Specifically, fine-tuning on fs1 provided consistent enhancements for the Qwen2.5-0.5B model, improving its CWQ score from 0.135 to 0.209. In contrast, the same fine-tuning on SmolLM2-360M yielded mixed results; while it improved performance on most tasks, it caused a notable degradation of -15.9% on GrailQA. This variance diminishes with scale, as models at the 1.5B/1.7B parameter scale exhibit more convergent behavior. For example, fine-tuning with rt on GrailQA provides a nearly identical small boost to both Qwen2.5-1.5B (+1.9%) and SmolLM2-1.7B (+1.8%). Overall, We hypothesize this scale-dependent effect may occur because larger models (e.g., 32B) possess stronger parametric knowledge, making them less reliant on the explicit guidance from KG paths. Table 3: Single Pass (pass@1) Results on mQA Benchmarks. We show accuracy and relative performance gains on our benchmarks for several baselines, Qwen2.5, and SmolLM2 models. For each size, we show the original instruction-tuned model followed by versions fine-tuned with chain-of-thought, rt, and fs1. Parentheses indicate the relative improvement over the instruction-tuned counterpart. The benefits of fine-tuning are most pronounced for smaller models. | Model Large Language Model Baselines Qwen2.5-72B | CWQ 0.481 | ExaQT 0.440 | GrailQA 0.361 | SimpleQA 0.117 | Mintaka 0.736 | WebQSP 0.653 | | --- | --- | --- | --- | --- | --- | --- | | QwQ-32B | 0.479 | 0.390 | 0.358 | 0.097 | 0.708 | 0.612 | | R1-70B | 0.501 | 0.476 | 0.340 | 0.188 | 0.755 | 0.549 | | o3-mini | 0.558 | 0.497 | 0.438 | 0.138 | 0.774 | 0.680 | | Small Language Models (0.36B-1.7B) | | | | | | | | SmolLM2-360M | 0.148 | 0.088 | 0.164 | 0.024 | 0.175 | 0.235 | | + cot | 0.151 (+2.0 %) | 0.101 (+14.8 %) | 0.169 (+3.0 %) | 0.025 (+4.2 %) | 0.188 (+7.4 %) | 0.230 (-2.1 %) | | + rt | 0.192 (+29.7 %) | 0.111 (+26.1 %) | 0.156 (-4.9 %) | 0.029 (+20.8 %) | 0.202 (+15.4 %) | 0.293 (+24.7 %) | | + fs1 | 0.179 (+20.9 %) | 0.093 (+5.7 %) | 0.138 (-15.9 %) | 0.027 (+12.5 %) | 0.197 (+12.6 %) | 0.264 (+12.3 %) | | Qwen2.5-0.5B | 0.135 | 0.058 | 0.127 | 0.023 | 0.131 | 0.173 | | + cot | 0.161 (+19.3 %) | 0.104 (+79.3 %) | 0.141 (+11.0 %) | 0.031 (+34.8 %) | 0.214 (+63.4 %) | 0.234 (+35.3 %) | | + rt | 0.190 (+40.7 %) | 0.089 (+53.4 %) | 0.155 (+22.0 %) | 0.022 (-4.3 %) | 0.178 (+35.9 %) | 0.286 (+65.3 %) | | + fs1 | 0.209 (+54.8 %) | 0.101 (+74.1 %) | 0.166 (+30.7 %) | 0.035 (+52.2 %) | 0.202 (+54.2 %) | 0.302 (+74.6 %) | | Qwen2.5-1.5B | 0.234 | 0.170 | 0.208 | 0.031 | 0.316 | 0.360 | | + cot | 0.252 (+7.7 %) | 0.179 (+5.3 %) | 0.216 (+3.8 %) | 0.041 (+32.3 %) | 0.318 (+0.6 %) | 0.391 (+8.6 %) | | + rt | 0.255 (+9.0 %) | 0.173 (+1.8 %) | 0.212 (+1.9 %) | 0.038 (+22.6 %) | 0.294 (-7.0 %) | 0.360 (+0.0 %) | | + fs1 | 0.263 (+12.4 %) | 0.162 (-4.7 %) | 0.204 (-1.9 %) | 0.035 (+12.9 %) | 0.301 (-4.7 %) | 0.356 (-1.1 %) | | SmolLM2-1.7B | 0.248 | 0.176 | 0.219 | 0.032 | 0.293 | 0.408 | | + cot | 0.285 (+14.9 %) | 0.177 (+0.6 %) | 0.209 (-4.6 %) | 0.032 (+0.0 %) | 0.295 (+0.7 %) | 0.409 (+0.2 %) | | + rt | 0.306 (+23.4 %) | 0.184 (+4.5 %) | 0.223 (+1.8 %) | 0.038 (+18.7 %) | 0.366 (+24.9 %) | 0.454 (+11.3 %) | | + fs1 | 0.305 (+23.0 %) | 0.179 (+1.7 %) | 0.218 (-0.5 %) | 0.036 (+12.5 %) | 0.341 (+16.4 %) | 0.426 (+4.4 %) | | Large Language Models (3B-32B) | | | | | | | | Qwen2.5-3B | 0.317 | 0.214 | 0.252 | 0.044 | 0.396 | 0.466 | | + cot | 0.302 (-4.7 %) | 0.222 (+3.7 %) | 0.248 (-1.6 %) | 0.048 (+9.1 %) | 0.431 (+8.8 %) | 0.477 (+2.4 %) | | + rt | 0.363 (+14.5 %) | 0.235 (+9.8 %) | 0.279 (+10.7 %) | 0.053 (+20.5 %) | 0.495 (+25.0 %) | 0.483 (+3.6 %) | | + fs1 | 0.330 (+4.1 %) | 0.205 (-4.2 %) | 0.253 (+0.4 %) | 0.045 (+2.3 %) | 0.444 (+12.1 %) | 0.406 (-12.9 %) | | Qwen2.5-7B | 0.376 | 0.281 | 0.299 | 0.070 | 0.548 | 0.580 | | + cot | 0.383 (+1.9 %) | 0.292 (+3.9 %) | 0.295 (-1.3 %) | 0.062 (-11.4 %) | 0.580 (+5.8 %) | 0.565 (-2.6 %) | | + rt | 0.401 (+6.6 %) | 0.296 (+5.3 %) | 0.300 (+0.3 %) | 0.067 (-4.3 %) | 0.576 (+5.1 %) | 0.517 (-10.9 %) | | + fs1 | 0.408 (+8.5 %) | 0.272 (-3.2 %) | 0.303 (+1.3 %) | 0.053 (-24.3 %) | 0.551 (+0.5 %) | 0.492 (-15.2 %) | | Qwen2.5-14B | 0.392 | 0.336 | 0.318 | 0.068 | 0.624 | 0.599 | | + cot | 0.422 (+7.7 %) | 0.356 (+6.0 %) | 0.322 (+1.3 %) | 0.080 (+17.6 %) | 0.664 (+6.4 %) | 0.592 (-1.2 %) | | + rt | 0.451 (+15.1 %) | 0.352 (+4.8 %) | 0.331 (+4.1 %) | 0.082 (+20.6 %) | 0.678 (+8.7 %) | 0.562 (-6.2 %) | | + fs1 | 0.454 (+15.8 %) | 0.339 (+0.9 %) | 0.328 (+3.1 %) | 0.079 (+16.2 %) | 0.654 (+4.8 %) | 0.558 (-6.8 %) | | Qwen2.5-32B | 0.428 | 0.362 | 0.334 | 0.087 | 0.674 | 0.621 | | + cot | 0.435 (+1.6 %) | 0.366 (+1.1 %) | 0.332 (-0.6 %) | 0.099 (+13.8 %) | 0.696 (+3.3 %) | 0.614 (-1.1 %) | | + rt | 0.471 (+10.0 %) | 0.366 (+1.1 %) | 0.342 (+2.4 %) | 0.094 (+8.0 %) | 0.680 (+0.9 %) | 0.563 (-9.3 %) | | + fs1 | 0.477 (+11.4 %) | 0.361 (-0.3 %) | 0.344 (+3.0 %) | 0.078 (-10.3 %) | 0.682 (+1.2 %) | 0.576 (-7.2 %) | 5 Related Work Different methods that involve long chain-of-thought processes (Kojima et al., 2022; Wei et al., 2022) involving reflection, backtracking, thinking (e.g., DeepSeek-AI, 2025; Muennighoff et al., 2025), self-consistency (e.g., Wang et al., 2023), and additional computation at inference time, such as test-time scaling (Wu et al., 2024; Muennighoff et al., 2025; Zhang et al., 2025), have shown promising improvements in LLM performance on complex reasoning tasks. Our work intersects with efforts in factuality, knowledge graph grounding, and test-time scaling. Graph-enhanced In-context Learning. Enhancing the factual consistency of LLMs using KGs has been explored in different directions, including semantic parsing methods that convert natural language questions into formal KG queries (Lan & Jiang, 2020; Ye et al., 2022). Retrieval-augmented methods (KG-RAG) (Li et al., 2023; Sanmartin, 2024; Jiang et al., 2023) aim to reduce LLMs’ reliance on latent knowledge by incorporating explicit, structured information from a KG; reasoning on graphs (RoG) models (Luo et al., 2024) generate relation paths grounded by KGs as faithful paths for the model to follow. Recent works like Tan et al. (2025) also use KG paths to guide reasoning, our fs1 approach focuses on distilling and fine-tuning general instruction models on these factually-grounded traces, and we provide a broad empirical study on its effect across model scales. Long Form Factuality. Factuality in NLP involves multiple challenges (Augenstein et al., 2024), and while prior efforts have established reasoning datasets like SAFE (Wei et al., 2024b) and SimpleQA (Wei et al., 2024a), they often lack explicit grounding in structured knowledge subgraphs. In contrast, Tian et al. (2024) directly address factual accuracy by fine-tuning models on automatically generated preference rankings that prioritize factual consistency. Test-Time Scaling as a Performance Upper-Bound. Our evaluation using pass@ $k$ is situated within the broader context of test-time scaling, which seeks to improve performance by dedicating more compute at inference. This field encompasses parallel scaling (e.g., Best-of-N), where multiple candidate solutions are generated to increase the probability of finding a correct one (Chollet, 2019; Irvine et al., 2023; Brown et al., 2024a; Li et al., 2022), and sequential scaling, where a single solution is iteratively refined through techniques like chain-of-thought prompting and revision (Wei et al., 2022; Nye et al., 2021; Madaan et al., 2023; Lee et al., 2025; Hou et al., 2025; Huang et al., 2023; Min et al., 2024; Muennighoff et al., 2025; Wang et al., 2024b; Li et al., 2025; Jurayj et al., 2025). While practical applications of parallel scaling depend on a selection mechanism (e.g., majority voting or reward-model-based scoring) to choose the final answer (Wang et al., 2023; Christiano et al., 2017; Lightman et al., 2024; Wang et al., 2024a; Wu et al., 2024; Beeching et al., 2025; Pan et al., 2024; Hassid et al., 2024; Stroebl et al., 2024), the performance of any such method is fundamentally limited by the quality of the underlying generations, often facing diminishing returns (Brown et al., 2024b; Snell et al., 2024; Wu et al., 2024; Levi, 2024). Our work, therefore, focuses on improving the quality of each individual reasoning trace through fine-tuning, thereby directly boosting the upper-bound potential that is measured by pass@ $k$ . Domain-specific Test-Time Scaling. Test-time scaling also spans specific domains like coding and medicine. Z1-7B optimizes coding tasks through constrained reasoning windows, reducing overthinking while maintaining accuracy (Yu et al., 2025). In medicine, extended reasoning boosts smaller models’ clinical QA performance significantly (Huang et al., 2025), complemented by structured datasets like MedReason, which enhance factual reasoning via knowledge-graph-guided paths (Wu et al., 2025), similar to our work. 6 Conclusion In this work, we have investigated whether grounding reasoning traces on knowledge graph paths and training models on them yield tangible gains in factual accuracy on complex open-domain QA tasks. After distilling over 3K original and knowledge-graph-enhanced reasoning traces from models such as QwQ-32B and Deepseek-R1, we fine-tuned 8 LLMs on rt and fs1 and evaluated them across 6 diverse benchmarks. In short, with parallel sampling, we consistently improve 6-14 absolute points in accuracy over their instruction-tuned counterpart. Particularly, using SimpleQA, we highlight that CoT and rt perform better on simpler questions (1 or 2 hops required), whereas our fs1 -tuned model performs better on more complex questions, requiring 3 hops or more. Lastly, we examined the performance of eight fs1 -tuned models across different parameter scales, finding that smaller models (below the 1.7B parameter range) show the largest increase in performance, while larger models see less profound improvements in a pass@1 setting. By releasing all code, models, and reasoning traces, we provide a rich resource for future work on process-level verification and the development of factuality-aware reward models. In turn, we hope this work facilitates more factual large language models, making them more useful for real-world usage. Limitations. Our approach assumes that conditioning on KG paths improves the accuracy of reasoning traces, though it does not guarantee perfect intermediate processes. Additionally, accurately evaluating entity answers poses challenges; we attempted to mitigate this limitation using LLM-based judgments, but these methods have their own inherent limitations. For evaluation, we note that pass@ $k$ is an upper-bound performance measure. A practical implementation would require an additional selection mechanism, such as majority voting or a verifier model, to choose the final answer. Last, some of the the test datasets used might be on the older side and English only, where we do not have control on whether the data has been included in any type of LLM pre- or post-training. Future Work. Several future research directions emerge from both the KG and test-time scaling perspectives. One promising avenue is leveraging these reasoning traces to develop process reward models, which are designed for complex reasoning and decision-making tasks where evaluating intermediate (factual reasoning) steps is critical to achieving the desired outcomes. This in fact is a crucial step towards more factual LLMs. This can be done together with KGs, one possible example could be (Amayuelas et al., 2025), where they attempt to ground every generation with a knowledge graph entry. This can possibly be done during the generation of long reasoning. Ethics Statement The primary ethical motivation for this research is to enhance the factuality and reliability of LLMs. By addressing the probability of these models to generate incorrect information, our work aims to contribute positively to the development of more trustworthy AI systems. We do not foresee any direct negative ethical implications arising from this research. Instead, our goal is to provide a methodology that mitigates existing risks associated with misinformation, thereby promoting a safer and more beneficial application of language technologies. Reproducibility Statement We are committed to full reproducibility. All associated artifacts, such as source code, datasets, and pretrained model weights, will be made publicly available via GitHub and the Huggingface Hub upon publication. Further details regarding the computational environment, including hardware specifications, software dependencies, and hyperparameters used for fine-tuning and inference, are documented in Section 3 and Appendix B. We acknowledge that minor variations in numerical results may arise from discrepancies in hardware or software versions; however, we have provided sufficient detail to allow for a faithful replication of our experimental setup. Acknowledgments MZ and JB are supported by the research grant (VIL57392) from VILLUM FONDEN. We would like to thank the AAU-NLP group for helpful discussions and feedback on an earlier version of this article. 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URL https://aclanthology.org/P16-2033/. - Yu et al. (2025) Zhaojian Yu, Yinghao Wu, Yilun Zhao, Arman Cohan, and Xiao-Ping Zhang. Z1: Efficient test-time scaling with code, 2025. URL https://arxiv.org/abs/2504.00810. - Zhang et al. (2025) Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, Lei Wang, Weixu Zhang, Zhihan Guo, Yufei Wang, Irwin King, Xue Liu, and Chen Ma. What, how, where, and how well? a survey on test-time scaling in large language models, 2025. URL https://arxiv.org/abs/2503.24235. Appendix A Large Language Model Use We made use of LLMs to polish our writing and plotting our figures. Appendix B Training and Inference For running Deepseek-R1, o3-mini, and some LLM-as-a-Judge experiments with gpt-4o-mini, we use API-based solutions via OpenAI https://platform.openai.com/ or TogetherAI https://api.together.ai/. The costs of running inference on all data took around 250 USD. For fine-tuning and running inference of the local models, we make use of a large HPC cluster with hardware configurations comprising multiple nodes (depending on model size; e.g., 32B models require 4 nodes for training and 1 node for inference), each with node contains eight AMD MI250x GPU modules alongside a single 64-core AMD EPYC “Trento” CPU. The library we use for inference is vllm (Kwon et al., 2023). For all the experiments it resulted in around 6,500 GPU hours spent. B.1 Environmental Impact We acknowledge that conducting a large-scale analysis using LLMs comes with an environmental impact. Experiments were conducted using private infrastructure in [Redacted] running on green energy. A cumulative of 6,500 GPU hours of computation was performed on AMD MI250x GPU modules, which has a TDP of 500 Watts. The experiments were ran from February to September 2025. During this time, the average carbon efficiency in [Redacted] was 0.085 $kg/kWh$ . According to https://app.electricitymaps.com/map. This means we released about 276 $kg$ of $CO_{2}$ equivalent. Estimations were conducted using the Machine Learning Impact calculator Find the tool here: https://mlco2.github.io/impact. presented in (Lacoste et al., 2019). Appendix C Example Reasoning Traces We show four full examples in Figure 8 and Figure 9 for rt and Figure 10 and Figure 11 for fs1. For visualization purposes, we split up the specific parts in special tags: <question>Q</question>, <think>T</think>, <gold>G</gold> to indicate what it refers to. Deepseek-R1 Example (rt) ⬇ < question > In Family Guy, Seth Macfarlane plays the parent of Chris Griffin. What is his character ’ s name? </ question > < think > Okay, so I need to figure out the name of the character Seth MacFarlane voices who is the parent of Chris Griffin in Family Guy. Let me start by recalling what I know about the show.\ n \ nFirst, Family Guy is an animated sitcom created by Seth MacFarlane. He ’ s known for voicing several characters in the show. The main family is the Griffins. The children in the family are Chris, Meg, and Stewie, and there ’ s also the talking dog Brian. The parents would be the father and mother of these kids.\ n \ nI remember that the father is Peter Griffin, who is the bumbling, overweight patriarch of the family. Seth MacFarlane does voice Peter Griffin, along with other characters like Stewie and Brian. The mother is Lois Griffin, but I think she ’ s voiced by Alex Borstein, not Seth MacFarlane. So, since the question is about the parent of Chris Griffin that Seth MacFarlane plays, it must be Peter Griffin.\ n \ nWait, let me double - check. Chris is the son, so his parents are Peter and Lois. Since Seth MacFarlane voices Peter, that ’ s the answer. But just to be thorough, are there any other parents in the show that Seth voices? For example, maybe a grandparent? But Chris ’ s grandparents would be Peter ’ s parents or Lois ’ s parents. I think Peter ’ s father is named Francis Griffin, but I don ’ t recall if Seth voices him. Lois ’ s parents are Carter and Babs Pewterschmidt, and they are voiced by other actors. So no, the main parent characters voiced by Seth would be Peter. \ n \ nAlso, Stewie is the baby, and Brian is the dog, both voiced by Seth, but they aren ’ t parents. So yes, the answer should be Peter Griffin. </ think > The character voiced by Seth MacFarlane who is the parent of Chris Griffin in * Family Guy * is \\ boxed {Peter Griffin}. < gold > [” Peter Griffin ”, ” Peter Lowenbrou Griffin ”] </ gold > Figure 8: Example Instance (rt). A successful reasoning trace for a simple multi-hop question by Deepseek-R1. QwQ-32B Example (rt) ⬇ < question > What Eastern Europe main religion worshipers also attend St. Mary ’ s Cathedral, in Batticaloa, Sri Lanka? </ question > < think > Okay, so the user is asking about the main religion worshippers in Eastern Europe who attend St. Mary ’ s Cathedral in Batticaloa, Sri Lanka. Hmm, first I need to break this down. Let me start by recalling that Batticaloa is a city in Sri Lanka, which is in South Asia. The cathedral mentioned is a Christian church, specifically a Catholic cathedral since it ’ s named St. Mary ’ s.\ n \ nNow, the question is about Eastern Europeans who worship there. Wait, Eastern Europe ’ s main religions are predominantly Christian, like Eastern Orthodox or Roman Catholic in some countries. But Sri Lanka ’ s main religions are Buddhism, Hinduism, Islam, and then there ’ s a Christian minority. So why would Eastern Europeans be attending a cathedral there? Maybe there ’ s a community of Eastern Europeans living or working in Batticaloa? Or perhaps it ’ s a question about the cathedral ’ s affiliation?\ n \ nAlternatively, maybe there ’ s confusion in the question. St. Mary ’ s Cathedral in Batticaloa is likely part of the Roman Catholic Church. Eastern Europeans who are Catholic, like from countries such as Poland, Lithuania, or parts of Croatia, might attend. But Eastern Orthodox Christians would typically go to their own churches. Since the cathedral is Catholic, the main worshippers there would be local Catholics, but the question specifies Eastern Europeans. \ n \ nWait, perhaps the user is thinking of the term \” Eastern Europe \” in a different context. Maybe they ’ re referring to the Eastern Catholic Churches, which are in communion with Rome but have Eastern rites. For example, Ukrainian Catholics or Ruthenian Catholics. But would they be in Sri Lanka? Maybe there ’ s a small expat community or missionary group. Alternatively, maybe the question is mixing up terms, and the answer is Roman Catholic, as that ’ s the cathedral ’ s denomination, and Eastern Europeans of that faith would attend. \ n \ nAlternatively, could there be a confusion with Batticaloa ’ s demographics? The area has a significant Tamil population, but Tamils are mostly Hindu or Christian. The cathedral serves the local Catholic community, which includes Sri Lankan Tamils and others. But the question specifically asks about Eastern Europeans. Maybe the answer is Roman Catholics from Eastern Europe, but I need to confirm if there ’ s a specific group. \ n \ nAlternatively, perhaps the question is a trick, and the main religion in Eastern Europe (like Orthodox) isn ’ t the one at the cathedral, so the answer is Roman Catholics. Since the cathedral is Catholic, Eastern European Catholics would be the ones attending. So the main religion worshippers there from Eastern Europe would be Roman Catholics. \ n \ nI think that ’ s the most logical answer here. The cathedral is Catholic, so Eastern Europeans who are Roman Catholic would attend. The main religions in Eastern Europe include Orthodox and Catholic, so the Catholic ones from there would be the answer. </ think > \\ boxed {Roman Catholics}. < gold > [” Catholicism ”, ” Roman Catholic convert ”, ” Roman Catholic ”] </ gold > Figure 9: Example Instance (rt). A successful reasoning trace for a simple multi-hop question by QwQ-32B. QwQ-32B Example (fs1) ⬇ < question > What geographic region is the country that used the Ottoman Lira used? While answering the question, make use of the following linearised graph as an inspiration in your reasoning, not as the only answer: Konstantin Mikhailovich Bazili, place of birth, Istanbul Istanbul, work location, Near East. Put your final answer within \ boxed {}. </ question > < think > Okay, let ’ s tackle this question step by step. The user is asking about the geographic region of the country that used the Ottoman Lira. First, I need to recall what the Ottoman Lira was. From what I remember, the lira was the currency used in the Ottoman Empire. So the country in question here is the Ottoman Empire. But since the Ottoman Empire is a historical entity and not a current country, the region would refer to where the empire was located. Now, the Ottoman Empire was a large and extensive empire that existed from the 14 th century until after World War I. Its heartland was in Anatolia (modern - day Turkey) and Southeast Europe. The capital was Constantinople, which is now known as Istanbul, Turkey. The empire also controlled much of the Balkans, parts of North Africa, and the Middle East. … (cutoff) … Wait, but the question is about the geographic region of the country that used the Ottoman Lira. The country is the Ottoman Empire. The geographic region that it is in is the Near East. So combining the clues provided (Istanbul being part of the Near East), the answer would be the Near East. Alternatively, if they want the specific country now, like Turkey, but since the Ottoman Empire was not Turkey, but Turkey is the successor state. The question is a bit ambiguous. However, given the clues pointing towards Near East as the work location linked to Istanbul, I think the intended answer is the Near East region. So the final answer would be Near East. </ think > The Ottoman Lira was the currency of the Ottoman Empire, which was centered in the geographic region known historically as the Near East. This region included the empire ’ s heartland in Anatolia (modern - day Turkey), Istanbul (then Constantinople), parts of Southeast Europe, and the Middle East. The provided clues about Istanbul, the former capital, and its association with the Near East further reinforce this connection. \\ boxed {Near East}. < gold > [” Near East ”] </ gold > Figure 10: Example Instance (fs1). A successful reasoning trace for a question by QwQ-32B. Deepseek-R1 Example (fs1) ⬇ < question > What part of the world map is the Government of the Republic of Seychelles located in? While answering the question, make use of the following linearised graph as an inspiration in your reasoning, not as the only answer: Seychellois Creole, country, Seychelles Seychelles, continent, Africa Seychellois Creole, indigenous to, Seychelles Seychelles, continent, Africa. Put your final answer within \ boxed {}. </ question > < think > Okay, so I need to figure out where the Government of the Republic of Seychelles is located on the world map. Let me start by recalling what I know about Seychelles. I think Seychelles is a country made up of islands. But where exactly? The user provided a linearised graph that mentions Seychelles is in the continent of Africa. Hmm, that ’ s interesting because when I think of Africa, I usually picture the mainland, but there are also island countries associated with Africa. Let me verify that. Seychelles is an archipelago in the Indian Ocean, right? So it ’ s a group of islands northeast of Madagascar. Since Madagascar itself is off the southeastern coast of Africa, Seychelles being northeast of there would place it in the Indian Ocean, near the African continent. The graph also mentions Seychellois Creole being indigenous to Seychelles, which makes sense as the local language. So, putting this together, the Government of the Republic of Seychelles is located in the country of Seychelles, which is part of the African continent. Even though it ’ s an island nation, geographically and politically, it ’ s considered part of Africa. Therefore, the answer should be that the government is located in Seychelles, which is in Africa. </ think > The Government of the Republic of Seychelles is located in the country of Seychelles. According to the provided graph, Seychelles is part of the continent of Africa. Although geographically situated in the Indian Ocean northeast of Madagascar, it is politically and regionally classified as part of Africa. \\ boxed {Africa} < gold > [ ” Africa ”, ” Afrique ” ] </ gold > Figure 11: Example Instance (fs1). A successful reasoning trace for a question by Deepseek-R1. Appendix D Test Benchmark D.1 Examples Test Benchmark In Figure 12, we show for each dataset in the test benchmark an example of how the question and answers look like. Benchmark Test Examples ⬇ < cwq > Who was the president in 2012 of the country where Unidad de Valor Constante is the chosen currency? Answers: [ ” Rafael Correa ”, ” Rafael Correa Delgado ”, ” Rafael Vicente Correa Delgado ” ] </ cwq > < exaqt > what nba team won the back to back championship in 1989 and 1990? Answer: [ ” Detroit Pistons ” ] </ exaqt > < grailqa > lonnie wheeler contributed to a book edition published by what publisher? Answer: [ ” Simon & Schuster ” ] </ grailqa > < simpleqa > Who received the IEEE Frank Rosenblatt Award in 2010? Answer: [ ” Michio Sugeno ” ] </ simpleqa > < mintaka > How many books are in Goosebumps? Answer: [ ”235” ] </ mintaka > < webqsp > where did diego velazquez die? Answer: [ ” Madrid ” ] </ webqsp > Figure 12: Text Examples. For each dataset in the benchmark, we show an example. Appendix E SPARQL queries The query format to retrieve the Wikidata entities for Freebase entities is given by SELECT ?wikientity WHERE {?wikientity wdt:P646 $FREEBASE_ENTITY} The general structure of the SPARQL queries for 2-hop paths between the source and target entities are given by SELECT ?p1 ?p1Label ?o1 ?o1Label ?p2 ?p2Label WHERE {wd:$SOURCE_ENTITY ?p1 ?o1. ?o1 ?p2 wd:$TARGET_ENTITY} SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],mul,en". } SELECT ?o1 ?o1Label ?p1 ?p1Label ?p2 ?p2Label WHERE { ?p1 ?o1. ?o1 ?p2 wd:$TARGET_ENTITY} SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],mul,en". } Appendix F Absolute Performance Results for Analysis We show the absolute performance numbers from Figure 7 at pass@ $16$ in Figure 13. <details> <summary>x8.png Details</summary>  ### Visual Description ## Bar Chart: Absolute Performance by Hops ### Overview The chart visualizes the "pass@16" performance metric across four categories of hops (1, 2, 3, 3+). Four metrics are compared: `inst` (gray), `cot` (light blue), `rt` (medium blue with diagonal stripes), and `fs1` (dark blue with diagonal stripes). Performance values range from 0.0 to 0.3 on the y-axis. ### Components/Axes - **X-axis**: Hops categories (`1`, `2`, `3`, `3+`), evenly spaced. - **Y-axis**: Performance metric (`pass@16`), scaled from 0.0 to 0.3 in increments of 0.1. - **Legend**: Located in the top-left corner, mapping colors to metrics: - `inst`: Gray - `cot`: Light blue - `rt`: Medium blue (diagonal stripes) - `fs1`: Dark blue (diagonal stripes) ### Detailed Analysis - **Category 1**: - `inst`: ~0.18 - `cot`: ~0.22 - `rt`: ~0.24 - `fs1`: ~0.23 - **Category 2**: - `inst`: ~0.22 - `cot`: ~0.25 - `rt`: ~0.32 - `fs1`: ~0.30 - **Category 3**: - `inst`: ~0.20 - `cot`: ~0.25 - `rt`: ~0.31 - `fs1`: ~0.32 - **Category 3+**: - `inst`: ~0.18 - `cot`: ~0.23 - `rt`: ~0.22 - `fs1`: ~0.26 ### Key Observations 1. **Performance Trends**: - `rt` and `fs1` consistently show the highest values across all categories. - `inst` has the lowest values in all categories. - Performance for `rt` and `fs1` peaks at `3` hops, then declines in `3+`. - `cot` remains relatively stable, with minor fluctuations. 2. **Outliers**: - `rt` in `3+` drops sharply to ~0.22, the lowest among all metrics in that category. - `fs1` in `3+` also declines but remains higher than `inst` and `cot`. ### Interpretation The data suggests that performance metrics (`rt` and `fs1`) improve with increasing hops up to 3, after which they degrade. This could indicate diminishing returns or system instability beyond 3 hops. The `inst` metric consistently underperforms, possibly reflecting a baseline or less optimized process. The stability of `cot` implies it is less sensitive to hop count variations. The sharp decline in `rt` and `fs1` at `3+` warrants further investigation into system constraints or resource limitations at higher hop counts. </details> (a) Performance by number of hops required to answer the question measured in pass@ $16$ . <details> <summary>x9.png Details</summary>  ### Visual Description ## Bar Chart: Absolute Performance by Answer Type ### Overview The chart visualizes the absolute performance (measured as "pass@16") across five answer types: **date**, **number**, **other**, **person**, and **place**. Four answer subtypes are compared: **inst** (gray), **cot** (yellow), **rt** (teal), and **fs1** (blue). Performance values range from 0.0 to 0.3 on the y-axis. ### Components/Axes - **X-axis**: Answer types (**date**, **number**, **other**, **person**, **place**). - **Y-axis**: Performance metric (**pass@16**), scaled from 0.0 to 0.3. - **Legend**: Located in the top-right corner, mapping colors to answer subtypes: - **inst**: Gray - **cot**: Yellow - **rt**: Teal - **fs1**: Blue - **Bars**: Grouped by answer type, with each subgroup representing an answer subtype. ### Detailed Analysis 1. **Date**: - **inst**: ~0.14 - **cot**: ~0.20 - **rt**: ~0.20 - **fs1**: ~0.21 2. **Number**: - **inst**: ~0.23 - **cot**: ~0.27 - **rt**: ~0.29 - **fs1**: ~0.30 3. **Other**: - **inst**: ~0.24 - **cot**: ~0.29 - **rt**: ~0.31 - **fs1**: ~0.31 4. **Person**: - **inst**: ~0.11 - **cot**: ~0.14 - **rt**: ~0.14 - **fs1**: ~0.13 5. **Place**: - **inst**: ~0.21 - **cot**: ~0.20 - **rt**: ~0.20 - **fs1**: ~0.19 ### Key Observations - **Highest Performance**: The **other** category consistently shows the highest values across all answer subtypes (~0.24–0.31). - **Lowest Performance**: The **person** category has the lowest values (~0.11–0.14). - **Trends**: - **inst** (gray) generally underperforms compared to other subtypes, except in **person**. - **cot** (yellow) and **rt** (teal) show similar trends, with **rt** slightly outperforming **cot** in most categories. - **fs1** (blue) exhibits the highest performance in **number** and **other** but drops in **person** and **place**. ### Interpretation The data suggests that **other** answer types are the most performant, potentially due to simpler or more structured patterns. **Person** answer types may involve greater complexity or ambiguity, leading to lower performance. The **fs1** subtype demonstrates robustness in **number** and **other** categories but struggles with **person** and **place**, indicating possible domain-specific limitations. The **inst** subtype’s lower performance across most categories highlights potential inefficiencies in its processing logic compared to other methods. </details> (b) Performance by answer type (i.e., what type of entity the answer is) in pass@ $16$ . <details> <summary>x10.png Details</summary>  ### Visual Description ## Bar Chart: Absolute Performance by Domain ### Overview The chart compares absolute performance metrics across 10 domains (art, geography, history, music, other, politics, sci & tech, sports, tv shows, video games) using four evaluation methods: inst, cot, rt, and fs1. Performance is measured on a scale from 0.0 to 0.3 (pass@16). Bars are grouped by domain, with each metric represented by distinct colors and patterns. ### Components/Axes - **X-axis**: Domains (art, geography, history, music, other, politics, sci & tech, sports, tv shows, video games) - **Y-axis**: Performance metric "pass@16" (0.0–0.3) - **Legend**: - **inst**: Gray (solid) - **cot**: Blue (solid) - **rt**: Purple (diagonal stripes) - **fs1**: Red (horizontal stripes) - **Legend Position**: Right-aligned, outside the chart ### Detailed Analysis 1. **Art Domain**: - inst: ~0.15 (gray) - cot: ~0.25 (blue) - rt: ~0.27 (purple) - fs1: ~0.24 (red) 2. **Geography Domain**: - inst: ~0.17 (gray) - cot: ~0.22 (blue) - rt: ~0.20 (purple) - fs1: ~0.24 (red) 3. **History Domain**: - inst: ~0.24 (gray) - cot: ~0.26 (blue) - rt: ~0.29 (purple) - fs1: ~0.29 (red) 4. **Music Domain**: - inst: ~0.18 (gray) - cot: ~0.23 (blue) - rt: ~0.22 (purple) - fs1: ~0.23 (red) 5. **Other Domain**: - inst: ~0.15 (gray) - cot: ~0.21 (blue) - rt: ~0.20 (purple) - fs1: ~0.22 (red) 6. **Politics Domain**: - inst: ~0.19 (gray) - cot: ~0.26 (blue) - rt: ~0.25 (purple) - fs1: ~0.28 (red) 7. **Sci & Tech Domain**: - inst: ~0.23 (gray) - cot: ~0.28 (blue) - rt: ~0.28 (purple) - fs1: ~0.27 (red) 8. **Sports Domain**: - inst: ~0.16 (gray) - cot: ~0.24 (blue) - rt: ~0.23 (purple) - fs1: ~0.24 (red) 9. **Tv Shows Domain**: - inst: ~0.11 (gray) - cot: ~0.15 (blue) - rt: ~0.17 (purple) - fs1: ~0.16 (red) 10. **Video Games Domain**: - inst: ~0.15 (gray) - cot: ~0.14 (blue) - rt: ~0.15 (purple) - fs1: ~0.16 (red) ### Key Observations - **inst** (gray) consistently shows the lowest performance across all domains, with values ranging from 0.11 (tv shows) to 0.24 (history). - **cot** (blue) and **rt** (purple) generally outperform inst, with cot peaking at 0.28 (sci & tech) and rt at 0.29 (history). - **fs1** (red) exhibits moderate performance, with the highest value at 0.29 (history) and the lowest at 0.16 (video games). - **History** and **sci & tech** domains show the highest overall performance for cot and fs1. - **Tv shows** and **video games** have the lowest performance across all metrics. ### Interpretation The data suggests that evaluation methods **cot** and **rt** generally yield higher performance scores than **inst** and **fs1**, particularly in domains like history and sci & tech. The consistent underperformance of **inst** across domains may indicate it is a less effective or more restrictive evaluation method. The **fs1** metric shows variability but remains competitive with cot and rt in most cases. The lowest scores in tv shows and video games suggest these domains may present unique challenges or require specialized evaluation approaches. The use of distinct patterns (stripes) for rt and fs1 aids in visual differentiation but may introduce minor readability challenges in printed formats. </details> (c) Performance measured per domain in pass@ $16$ . Figure 13: Absolute Performance across Different Axes. We show the absolute performance at pass@ $16$ of different versions of Qwen-32B (i.e., instruct, CoT, rt and fs1). In (a), we show the performance of the models by answer type. In (b), we show the performance of the models by the number of hops required to answer the question. In (c), we show the performance by domain of the question.