LLM plans for long-horizon robot tasks often break embodiment constraints, fail to recover from mistakes, or lose track of objects they cannot see. GAVEL adds an explicit graph world model around the LLM and more than doubles the success rate of a small model without changing its weights.
A graph that tracks the world: The graph holds object relations, action preconditions and effects, and probabilistic beliefs about where unobserved objects are. Before the robot executes an LLM-generated action, the graph predicts what the action would do.
Repair without calling the model: It catches violations before execution. When the fix follows directly from the world model, GAVEL repairs it on its own, and only errors that need semantic reasoning go back to the LLM for replanning.
Large gains on BEHAVIOR-1K: With Qwen3-8B, single-task success rises from 41.2% to 91.8% across 100 long-horizon tasks, and multi-task success rises from 19.9% to 92.6% across 500 instructions. Reasoning over the distribution of possible object locations also reorders subtasks and cuts travel distance by about 5.4%.
Why it matters: Many long-horizon agent failures come from losing track of state rather than from weak reasoning. A symbolic model outside the LLM catches those failures cheaply, and the same idea applies to software agents that need to track the state of files, tickets, or accounts.
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More agents now store long-term memory as an LLM Wiki, a folder of markdown pages linked to each other. Each page holds dense text, and the links provide structure; WFM is a Wiki Foundation Model trained to use both when retrieving.
Wiki as a graph: WFM formalizes a Wiki Graph schema in which entity relations and passage nodes live in one graph. The explicit links stay intact while each page keeps its full text, which is harder to capture with sparse graph embeddings.
Query-conditioned retrieval: Retrieval runs message passing over the graph, conditioned on the query, with attentive aggregation and a regularizer on attention variance. Page text and link structure shape the result together.
Faster distributed training: The team built a GPU-to-GPU exchange protocol over NCCL that avoids CPU serialization and memory copies when the graph is split across GPUs. Training runs 10.5x faster on distributed clusters, targeting the overhead that makes graph encoders hard to deploy at scale.
Why it matters: WFM reports strong results on five long-term agent memory and multi-hop reasoning benchmarks. If your agent’s memory is already a folder of linked markdown files, this retrieval model is designed for that format instead of one adapted from plain RAG or sparse knowledge graphs.
