arXiv:2607. 20459v1 Announce Type: cross Abstract: Multi-hop question answering requires retrieving and integrating evidence from multiple contexts.
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arXiv:2410.17021v2 Announce Type: replace Abstract: Large Language Models with chain-of-thought prompting, such as OpenAI-o1, have shown impressive capabilities in natural language inference tasks. H...
arXiv:2601.08747v3 Announce Type: replace-cross Abstract: Current context augmentation methods, such as retrieval-augmented generation, play a crucial role in bridging a model's internal knowledge bo...
arXiv:2506.21734v4 Announce Type: replace Abstract: Reasoning, the process of devising and executing complex goal-oriented action sequences, remains a critical challenge in AI. Current large language...
The paper proposes PAIR, a method that treats reasoning paths of large language models as phase‑structured trajectories within each question. By sampling multiple trajectories per question, aligning them to shared relative phases, and comparing successful versus unsuccessful paths only within the same phase, PAIR isolates path‑quality signals from question‑level variation. Experiments show that standard correctness probes lose predictive power under this within‑question evaluation, while PAIR improves trajectory ranking, Best‑of‑N selection, and enables phase‑wise steering of generation outcomes.
The paper investigates how small language models (SLMs) perform in knowledge graph question answering (KGQA) when evaluated on the reasoning paths they take, rather than just the final answer. Using the THESEUS navigation and traceability framework, the authors test frozen, off‑the‑shelf SLMs as local action policies that choose graph actions and decide when to stop, without any task‑specific training or free‑form answer generation. By measuring both Hits@1 and Path Edit Distance (PED) across the Kinship and MQuAKE‑ST datasets, the study finds that models vary significantly in both answer accuracy and path fidelity, and that prompting can either help or hurt navigation depending on the model. "whyItMatters":"The results show that evaluating SLMs solely on endpoint accuracy can be misleading, highlighting the need to assess reasoning path fidelity in KGQA tasks."
arXiv:2608.22132v1 Announce Type: cross Abstract: Biomedical multi-hop question answering (QA) requires models to connect evidence across intermediate entities such as diseases, drugs, proteins, and...