InsightEmb: Learning Action-Intent Embeddings for Agentic Insight Retrieval

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Tsz Ting Chung, Jiangnan Li, Jie Zhou, Mo Yu
• Venue: arXiv: cs.CL
• Published: 2026-08-05
• Collected: 2026-08-06
• URL: http://arxiv.org/abs/2608.04761v1
• PDF: https://arxiv.org/pdf/2608.04761v1
• DOI: UNKNOWN

Plain-Language Brief

The paper proposes a new method called InsightEmb for learning action-intent embeddings to improve agentic insight retrieval, which helps self-improving agents make better decisions by using insights from prior experiences. The method learns transferable progress-oriented retrieval geometry using mathematical reasoning data without needing environment-specific training.

What the Document Says

• The paper presents InsightEmb, a contrastive embedding framework for learning action-intent embeddings.
• InsightEmb is designed for agentic insight retrieval, which helps agents progress towards their goals by retrieving the right insights at each decision step.
• Existing retrieval methods primarily model semantic similarity, but InsightEmb also considers whether a retrieved insight resolves the agent's current decision bottleneck.
• InsightEmb learns to align concrete situations with abstract heuristic rules and cluster reasoning trajectories with similar progress structures.
• The authors evaluate InsightEmb on dynamic agent tasks and a static skill-retrieval benchmark.
• Without any environment-specific training, InsightEmb improves over all evaluations.
• InsightEmb surpasses the performance of existing reasoning embedding models.
• The results suggest that the geometry of state-insight matching can transfer across domains, enabling effective training from publicly available reasoning data.

Key Concepts Mentioned

• agentic insight retrieval
• InsightEmb
• transferable progress-oriented retrieval geometry
• mathematical reasoning data
• dynamic agent tasks
• static skill-retrieval benchmark
• semantic similarity

Claims Made by the Document

• InsightEmb improves over all evaluations.
• InsightEmb surpasses the performance of existing reasoning embedding models.

Evidence Used

• The authors report that without any environment-specific training, InsightEmb improves over all evaluations and surpasses the performance of existing reasoning embedding models.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

Self-improving agents accumulate reusable insights from prior trajectories, making retrieval increasingly important for turning accumulated experience into actionable guidance. At each decision step, retrieving the right insight can help the agent progress toward its goal, a setting we refer to as agentic insight retrieval. However, existing retrieval methods primarily model semantic similarity, while overlooking whether a retrieved insight resolves the agent's current decision bottleneck. We propose InsightEmb, a contrastive embedding framework that learns transferable progress-oriented retrieval geometry using only mathematical reasoning data. InsightEmb jointly learns to align concrete situations with abstract heuristic rules and to cluster reasoning trajectories with similar progress structures. We evaluate InsightEmb on dynamic agent tasks and a static skill-retrieval benchmark. Without any environment-specific training, InsightEmb improves over all these evaluations, surpassing the performance of existing reasoning embedding models. These results suggest that the geometry of state-insight matching can transfer across domains, enabling effective training from publicly available reasoning data without expensive environment-specific supervision.