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ARXIV:2603.09400 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.09400AGENTSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
StateFactory transforms unstructured observations into structured representations for accurate reward prediction across diverse domains.
Opportunity summary
Pain StateFactory transforms unstructured observations into structured representations for accurate reward prediction across diverse domains.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
StateFactory transforms unstructured observations into structured representations for accurate reward prediction across diverse domains. Supervised learning of reward models could introduce biases inherent to training data, limiting generalization to novel goals and environments.
Agents must infer action outcomes and select actions that maximize a reward signal indicating how close the goal is to being reached. Supervised learning of reward models could introduce biases inherent to training data,…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. In this paper, we investigate whether well-defined world state representations alone can enable accurate reward prediction across domains.
Agents moved forward this cycle; last verified April 2026. Public score 8.0/10.
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StateFactory transforms unstructured observations into structured representations for accurate reward prediction across diverse domains.
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10.48550/arXiv.2603.09400StateFactory transforms unstructured observations into structured representations for accurate reward prediction across diverse domains.
Abstract
Agents must infer action outcomes and select actions that maximize a reward signal indicating how close the goal is to being reached. Supervised learning of reward models could introduce biases inherent to training data, limiting generalization to novel goals and environments. In this paper, we investigate whether well-defined world state representations alone can enable accurate reward prediction across domains. To address this, we introduce StateFactory, a factorized representation method that transforms unstructured observations into a hierarchical object-attribute structure using language models. This structured representation allows rewards to be estimated naturally as the semantic similarity between the current state and the goal state under hierarchical constraint. Overall, the compact representation structure induced by StateFactory enables strong reward generalization capabilities. We evaluate on RewardPrediction, a new benchmark dataset spanning five diverse domains and comprising 2,454 unique action-observation trajectories with step-wise ground-truth rewards. Our method shows promising zero-shot results against both VLWM-critic and LLM-as-a-Judge reward models, achieving 60% and 8% lower EPIC distance, respectively. Furthermore, this superior reward quality successfully translates into improved agent planning performance, yielding success rate gains of +21.64% on AlfWorld and +12.40% on ScienceWorld over reactive system-1 policies and enhancing system-2 agent planning. Project Page: https://statefactory.github.io
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 8.0
PROBLEM
StateFactory transforms unstructured observations into structured representations for accurate reward prediction across diverse domains. Supervised learning of reward models could introduce biases inherent to training data, limiting generalization to novel goals and environments.
METHOD
Agents must infer action outcomes and select actions that maximize a reward signal indicating how close the goal is to being reached. Supervised learning of reward models could introduce biases inherent to training data, limiting generalization to novel goals and environments.
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. In this paper, we investigate whether well-defined world state representations alone can enable accurate reward prediction across domains.
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 8.0/10.
introduce StateFactory, a factorized representation method that transforms unstructured observations into a hierarchical object-attribute structure using language models
Directly stated in abstract as the core method description
partial
This structured representation allows rewards to be estimated naturally as the semantic similarity between the current state and the goal state under hierarchical constraint
Directly stated in abstract as a key technical mechanism
partial
achieving 60% and 8% lower EPIC distance, respectively
Direct numeric result stated in abstract with specific metric
partial
yielding success rate gains of +21.64% on AlfWorld and +12.40% on ScienceWorld over reactive system-1 policies
Direct numeric result stated in abstract with specific benchmark
partial
yielding success rate gains of +21.64% on AlfWorld and +12.40% on ScienceWorld over reactive system-1 policies
Direct numeric result stated in abstract with specific benchmark
partial
the compact representation structure induced by StateFactory enables strong reward generalization capabilities
Directly stated in abstract as a key benefit, though 'strong' is qualitative
partial
Supervised learning of reward models could introduce biases inherent to training data, limiting generalization to novel goals and environments
Directly stated as motivation for the research, though presented as a general limitation of existing approaches
partial
Our method shows promising zero-shot results against both VLWM-critic and LLM-as-a-Judge reward models, achieving 60% and 8% lower EPIC distance, respectively
Direct numeric result stated in abstract with specific comparison
partial
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StateFactory transforms unstructured observations into structured representations for accurate reward prediction across diverse domains.
Segment
Agents
Adoption evidence
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Commercial read
8.0/10 public viability
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reason
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proof status
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Technical feasibility
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DEFENSIBILITY
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