arXiv AI
ImplicitHop: Evaluating knowledge integration across context and memory
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Finding the Minimal Parameter Budget for Implicit Reasoning: A Data Complexity Driven Scaling Law for Language Models
arXiv:2504. 03635v4 Announce Type: replace Abstract: Reasoning is a core capability of language models (LMs), yet it remains unclear how much model capacity is necessary to support reasoning during pretraining.
arXiv AI
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Collaborative Learning and Reasoning over Model Slices
The paper introduces collaborative learning for large language models, where a single large model is replaced by smaller, heterogeneous models—called model slices—under a comparable parameter budget. By enabling prior reasoning to be reassessed across different model parameterizations and employing pyramid supervision that blends protocol distillation with step‑level supervision, the authors refine reasoning and interaction decisions during collaboration. Experiments on multi‑hop question answering, mathematical reasoning, code generation, and open‑ended constrained generation show that the trained heterogeneous models match or surpass the performance of the original large model and established multi‑agent baselines, while also achieving higher inference efficiency and exhibiting task‑specific collaboration patterns.
arXiv AI
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Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers
arXiv:2604. 07822v2 Announce Type: replace-cross Abstract: We study implicit reasoning, i.
arXiv AI
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From Storage to Access: Verifiable Activation of Parametric Knowledge in LLMs via Explicit Priming and Implicit Reasoning
The paper introduces VAKE, a two‑stage reinforcement‑learning framework that activates latent factual knowledge in large language models. In the Priming stage, the model explicitly inserts bridging triples into an insufficient subgraph, guided by rewards from a frozen model’s answers. The Reasoning stage then trains the model to answer from the original input, demonstrating that the elicitation capability transfers to implicit reasoning and consistently outperforms baselines across multiple benchmarks and model sizes.
arXiv AI
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Foresight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answering
arXiv:2610.08388v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in question answering, yet they still frequently suffer from hallucinations on kno...
arXiv AI
Sep 1
RACER: Reinforced Agent Collaboration for Explainable Reasoning on Knowledge Graphs
arXiv:2608.29263v1 Announce Type: new Abstract: Large Language Models (LLMs) often suffer from hallucination and struggle with complex reasoning tasks requiring multi-hop domain knowledge. While inte...