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Evaluating claim admission in shared agent memory is challenging because repeated claims may be mistaken for independent evidence. An agent may copy or paraphrase a retrieved belief, while admitting a false claim exposes subsequent agents to it. To study this problem, we introduce the Correlated Promotion Benchmark (CP...
This work proposes a EA foundation model driven by a parallel encoding strategy that facilitates anchor-conditioned message passing and significantly shortens the inference trajectory by leveraging local structural proximity instead of global search.
A dense process supervision method based on fact utility estimation, which models the reasoning process as the accumulation of discrete evidence facts, and converts the estimated fact utilities into dense step-level rewards to guide RL training.
ProMem is proposed, a proactive memory extraction framework that separates details, events, and relations, and uses different extraction strategies for each type, and improves memory completeness and QA accuracy, while keeping a good balance between quality and token cost.
The LLM–KGFR collaborative framework, where an LLM works with a structured retriever, the Knowledge Graph Foundation Retriever (KGFR), achieves strong performance while maintaining scalability and generalization, providing a practical solution for KG-augmented reasoning.
AOPD replaces ineffective negative reinforcement with localized divergence minimization in non-positive advantage regions while preserving positive reinforcement learning and maintains higher policy entropy during training and better capability retention during sequential tool-use adaptation.
OaK is presented, an ontology-as-a-kernel framework that dynamically constructs and refines task-oriented ontologies for LLM agents and shows that OaK improves standard LLM agents, strengthens evidence grounding, and boosts the reliability of multi-step reasoning.
MAP-Graph is introduced, a provenance-aware memory layer that represents agents, sources, memories, claims, and actions in a typed execution graph and supports provenance as an operational control signal, rather than only post-hoc audit metadata, within the evaluated setting.
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