The ability to integrate new information and deduce its consequences is central to learning, understanding, and reasoning in large language models (LLMs). Whether information arrives during training or in-context, models must not only store local facts, but propagate their long-range implications globally. To explore information integration in LLMs, we introduce two complementary natural language benchmarks for large-scale evaluation of deductive reasoning as a function of inference depth. The first is a formal graph benchmark enabling controlled evaluation over a wide range of graph topologies and integration tasks; the second is a knowledge composition benchmark grounded in real-world knowledge graphs. We use these benchmarks to compare knowledge integration from information encoded in context versus information encoded in model weights during pretraining, evaluating a set of open models implicitly, without chain of thought. We find that accuracy decays sharply with increased composition length for both pretrained and in-context knowledge. Information presented in-context, however, substantially outperforms deduction over information encoded in weights. These results quantify a fundamental limitation of current models with direct implications for prompt-based control of agent behavior and for continual learning strategies, and they provide a foundation for developing training, prompting, and integration methods that support deeper reasoning.
Figures & tables
Figure 1: Models receive graph adjacency in-context but integrate the information poorly, failing at long-range inferences. DeepSeek, Kimi, Nemotron, and 12 other models answer questions about a random graph of 50–60 nodes given in the prompt as a list of edges, with reasoning off. Each question concerns the nodes exactly k hops from a start node, so k=1 questions require only direct-neighbor information. a) Verify: is a given node exactly k hops away? Accuracy falls from 93 – 100% at k=1 to 49 – 56% at k=3 , close to chance. b) Count: how many nodes are exactly k hops away? Models count correctly 43 – 69% of the time at k=1 and ≤16% from k=2 . c) Name one: name a node exactly k hops away. Models name a neighbor at 99 – 100% , but a node two hops away at only 9 – 40% . d) Enumerate: list every node exactly k hops away. Models list all neighbors at 71 – 100% , but no model lists more than 12% of sets correctly from k=2 .
Figure 2: Models compose facts from the prompt far better than from memory. DeepSeek, Kimi, and Nemotron answer k -hop Wikidata questions with reasoning off and information given in context, in memory, or both. a) The chain’s facts are given in the prompt under invented names, among 16 decoy chains with the same relations. All models compose at ≥94% up to k=3 , but at k=6 , Kimi composes at 29% , while DeepSeek and Nemotron Ultra fall to chance. b) The facts are recalled from memory without supporting facts in the prompt. Composition reaches only 37 – 64% at k=2 and ≤12% from k=4 , although every fact is known. c) The facts are given in the prompt under their real names, among 16 real decoy chains, so the model can use both the prompt and memory. Accuracy reaches 36 – 71% at k=6 .