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ARXIV:2602.22452 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.22452AGENTSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Develop action feasibility scorers for embodied agents using contrastive learning to improve reliability and safety in AI action planning.
Opportunity summary
Pain Develop action feasibility scorers for embodied agents using contrastive learning to improve reliability and safety in AI action planning.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Develop action feasibility scorers for embodied agents using contrastive learning to improve reliability and safety in AI action planning. Existing approaches use supervised fine-tuning (SFT) to train action scorers, but SFT treats each candidate…
A reliable action feasibility scorer is a critical bottleneck in embodied agent pipelines: before any planning or reasoning occurs, the agent must identify which candidate actions are physically executable in the current state. Existing…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. First, an intrinsic affordance evaluation on 605 hard-negative test pairs shows that CWM outperforms SFT by +6.76 percentage points on Precision@1 for minimal-edit negatives…
Agents moved forward this cycle; last verified April 2026. Public score 6.0/10.
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Develop action feasibility scorers for embodied agents using contrastive learning to improve reliability and safety in AI action planning.
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10.48550/arXiv.2602.22452Develop action feasibility scorers for embodied agents using contrastive learning to improve reliability and safety in AI action planning.
Abstract
A reliable action feasibility scorer is a critical bottleneck in embodied agent pipelines: before any planning or reasoning occurs, the agent must identify which candidate actions are physically executable in the current state. Existing approaches use supervised fine-tuning (SFT) to train action scorers, but SFT treats each candidate independently and does not explicitly teach the model to discriminate between actions that are physically correct and those that are subtly wrong. We propose the Contrastive World Model (CWM), which fine-tunes a large language model (LLM) as an action scorer using an InfoNCE contrastive objective with hard-mined negative examples. The key idea is to push valid actions away from invalid ones in scoring space, with special emphasis on hard negatives: semantically similar but physically incompatible candidates. We evaluate CWM on the ScienceWorld benchmark through two studies. First, an intrinsic affordance evaluation on 605 hard-negative test pairs shows that CWM outperforms SFT by +6.76 percentage points on Precision@1 for minimal-edit negatives -- cases where a single word changes the physical outcome -- and achieves a higher AUC-ROC (0.929 vs. 0.906). Second, a live filter characterisation study measures how well CWM ranks gold-path actions against all valid environment actions during task execution. Under out-of-distribution stress conditions, CWM maintains a significantly better safety margin (-2.39) than SFT (-3.96), indicating that the gold action is ranked closer to the top. These results support the hypothesis that contrastive training induces representations that capture physical feasibility more faithfully than SFT alone.
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Dimensions overall score 6.0
PROBLEM
Develop action feasibility scorers for embodied agents using contrastive learning to improve reliability and safety in AI action planning. Existing approaches use supervised fine-tuning (SFT) to train action scorers, but SFT treats each candidate independently and does not expli...
METHOD
A reliable action feasibility scorer is a critical bottleneck in embodied agent pipelines: before any planning or reasoning occurs, the agent must identify which candidate actions are physically executable in the current state. Existing approaches use supervised fine-tuning (SFT...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. First, an intrinsic affordance evaluation on 605 hard-negative test pairs shows that CWM outperforms SFT by +6.76 percentage points on Precision@1 for minimal-edit negatives -- cases where a single word c...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Develop action feasibility scorers for embodied agents using contrastive learning to improve reliability and safety in AI action planning. Existing approaches use supervised fine-tuning (SFT) to train action scorers, but SFT treats each candidate independently and does not explicitly teach the model to discriminate between actions that are physically correct and those that are subtly wrong.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
A reliable action feasibility scorer is a critical bottleneck in embodied agent pipelines: before any planning or reasoning occurs, the agent must identify which candidate actions are physically executable in the current state. Existing approaches use supervised fine-tuning (SFT) to train action scorers, but SFT treats each candidate independently and does not explicitly teach the model to discriminate between actions that are physically correct and those that are subtly wrong.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. First, an intrinsic affordance evaluation on 605 hard-negative test pairs shows that CWM outperforms SFT by +6.76 percentage points on Precision@1 for minimal-edit negatives -- cases where a single word changes the physical outcome -- and achieves a higher AUC-ROC (0.929 vs.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Develop action feasibility scorers for embodied agents using contrastive learning to improve reliability and safety in AI action planning.
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