OntoAct is a hypothesis‑based neuro‑symbolic framework that uses ontological abstraction to convert individual experiences into reusable axioms about entity types, relations, and conditions. A large language model proposes hypotheses and actions, while a symbolic verifier checks these against current observations, identifying missing evidence and updating assessments based on new data. The framework is evaluated on Tacit‑FLE, Mars, and ALFWorld, achieving leading task performance with efficient interaction and inference.
arXiv:2606. 07127v1 Announce Type: new Abstract: Interactive agents trained only against task return can achieve high scores while failing to represent the mechanisms that make their actions succeed.
arXiv:2605. 16309v2 Announce Type: replace Abstract: LLM-based agents can recover from individual execution errors, yet they repeatedly fail on the same fault when the underlying process knowledge--operator schemas, preconditions, and constraints--remains unrepaired.
The paper introduces AMBER, an append‑only memory framework for language‑model agents that interact over long horizons. AMBER lets agents jointly learn to reason, act, and write free‑form memory, guaranteeing retention by construction and enabling end‑to‑end reinforcement learning without extensive curated data. Experiments on WebArena Lite show AMBER outperforms overwrite‑based memory by 4.09 percentage points in average success and improves task completion rates in repeated runs.
arXiv:2607. 22385v1 Announce Type: cross Abstract: Diagnosing the root cause of anomalies is essential for safe industrial operation.
arXiv:2606. 12852v1 Announce Type: new Abstract: Rapid advances have been made in developing general-purpose embodied agent in environments like Minecraft through the adoption of LLM-augmented hierarchical approaches.
The paper introduces a self-demonstration-driven method for mapping relational database schemas to ontologies, addressing challenges such as semantic heterogeneity and cryptic schema naming. It combines neuro-symbolic task decomposition with pattern-guided, dependency-aware demonstrations to improve LLM performance on schema-ontology mapping. Experiments on the RODI benchmark show state‑of‑the‑art results, outperforming existing methods by up to 25 percentage points in F1 score.
The paper introduces RIKA, a method for reliable knowledge acquisition in continual learning agents. RIKA hierarchically extracts factual, conceptual, procedural, and metacognitive knowledge and uses evidence-based admission to decide which claims are incorporated into the agent’s context, avoiding the reuse of unreliable information. Experiments on six task streams and four model backbones show that RIKA outperforms existing memory and experience-learning systems on aggregate performance.
arXiv:2607. 01595v1 Announce Type: new Abstract: As the scale and complexity of cloud-based AI systems continue to escalate, ensuring service reliability through rapid fault detection and adaptive recovery has become a critical challenge.
The paper introduces HypoAgent, an agentic framework for refining abductive hypotheses over knowledge graphs. It treats user-provided conditions as steering operators, using a small generator for initial proposals, root-cause analysis to identify problematic branches, and a refiner agent that revises hypotheses based on diagnosis and prior proposals. Experiments on BioKG, PharmKG8k, and DBpedia50 show HypoAgent surpasses one-shot generation in both single-turn and multi-turn scenarios, including unconditional settings.
MemPlan is a memory‑conditioned PDDL planning framework designed for partially observable interactive text environments. It keeps the PDDL domain fixed and reconstructs the problem at each step using parsed observations, candidate hypotheses, and interaction memory, employing positive graph memory to assign relevance‑based action costs and negative memory to record refuted assumptions as exclusion constraints. Experiments on TextWorld, ALFWorld, and Robotouille demonstrate that MemPlan improves task completion and step efficiency over language‑only and PDDL‑aware baselines while significantly reducing online token cost compared with LLM‑planning baselines such as ReAct, and its fine‑tuned interface generalizes across task types without task‑specific fine‑tuning.
arXiv:2610.14381v1 Announce Type: new Abstract: Large language models are increasingly used as scientific assistants, yet we still lack reliable ways to evaluate whether they can investigate a system...
The paper introduces AgenticDomiKnowS (ADS), a framework that converts free‑form task descriptions into fully functional DomiKnowS neuro‑symbolic programs. ADS employs an agentic workflow that builds and tests each DomiKnowS component independently, optionally allowing human‑in‑the‑loop refinement. The authors demonstrate that ADS enables both experienced and novice users to create complete neuro‑symbolic programs in 10–15 minutes, compared to the hour required for manual coding.