Event-graph substrates represent states as RDF triple logs, prove a duality reducing explanatory and counterfactual queries to causal-ancestor traversal, and outperform symbolic and parametric baselines on CLEVRER and a new Smallville benchmark.
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7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 7representative citing papers
Goal clarifications lose nearly all value after 10% of execution while input clarifications retain value until roughly 50%, and asking any type past mid-trajectory hurts performance more than never asking.
Dialogue between partially-observing LLM agents cuts action conflicts by 40-83 points but lowers task success versus silent coordination, with new metrics exposing limited genuine world-model alignment.
AI agents on Moltbook reflect the specific behavioral traits of their linked human owners across multiple dimensions, with stronger transfer linked to greater privacy risks.
LLARS is a new integrated platform that combines collaborative prompt authoring, cost-controlled batch generation, and hybrid evaluation to help domain experts and developers jointly build and assess LLM systems.
AlphaEarth embeddings form a rotating 13-dimensional manifold where local geometry predicts retrieval quality, and an agentic system using nine geometric tools outperforms parametric reasoning on environmental queries.
citing papers explorer
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Deterministic Event-Graph Substrates as World Models for Counterfactual Reasoning
Event-graph substrates represent states as RDF triple logs, prove a duality reducing explanatory and counterfactual queries to causal-ancestor traversal, and outperform symbolic and parametric baselines on CLEVRER and a new Smallville benchmark.
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Ask Early, Ask Late, Ask Right: When Does Clarification Timing Matter for Long-Horizon Agents?
Goal clarifications lose nearly all value after 10% of execution while input clarifications retain value until roughly 50%, and asking any type past mid-trajectory hurts performance more than never asking.
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Embodied Multi-Agent Coordination by Aligning World Models Through Dialogue
Dialogue between partially-observing LLM agents cuts action conflicts by 40-83 points but lowers task success versus silent coordination, with new metrics exposing limited genuine world-model alignment.
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Behavioral Transfer in AI Agents: Evidence and Privacy Implications
AI agents on Moltbook reflect the specific behavioral traits of their linked human owners across multiple dimensions, with stronger transfer linked to greater privacy risks.
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LLARS: Enabling Domain Expert & Developer Collaboration for LLM Prompting, Generation and Evaluation
LLARS is a new integrated platform that combines collaborative prompt authoring, cost-controlled batch generation, and hybrid evaluation to help domain experts and developers jointly build and assess LLM systems.
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Characterizing AlphaEarth Embedding Geometry for Agentic Environmental Reasoning
AlphaEarth embeddings form a rotating 13-dimensional manifold where local geometry predicts retrieval quality, and an agentic system using nine geometric tools outperforms parametric reasoning on environmental queries.
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