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ARXIV:2603.12368 · GENERATIVE RETRIEVAL · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.12368GENERATIVE RETRIEVALSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
ReasonGR enhances generative retrieval models by improving multi-step semantic reasoning for complex numerical queries.
Opportunity summary
Pain ReasonGR enhances generative retrieval models by improving multi-step semantic reasoning for complex numerical queries.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
ReasonGR enhances generative retrieval models by improving multi-step semantic reasoning for complex numerical queries. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex queries in numerical contexts, such as…
Generative retrieval (GR) models encode a corpus within model parameters and generate relevant document identifiers directly for a given query. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex queries in numerical contexts, such as those involving semantic reasoning…
Generative Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10.
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ReasonGR enhances generative retrieval models by improving multi-step semantic reasoning for complex numerical queries.
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Paper Pack
10.48550/arXiv.2603.12368ReasonGR enhances generative retrieval models by improving multi-step semantic reasoning for complex numerical queries.
Abstract
Generative retrieval (GR) models encode a corpus within model parameters and generate relevant document identifiers directly for a given query. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex queries in numerical contexts, such as those involving semantic reasoning over financial reports, due to limited reasoning capabilities. This limitation leads to suboptimal retrieval accuracy and hinders practical applicability. We propose ReasonGR, a framework designed to enhance multi-step semantic reasoning in numerical contexts within GR. ReasonGR employs a structured prompting strategy combining task-specific instructions with stepwise reasoning guidance to better address complex retrieval queries. Additionally, it integrates a reasoning-focused adaptation module to improve the learning of reasoning-related parameters. Experiments on the FinQA dataset, which contains financial queries over complex documents, demonstrate that ReasonGR improves retrieval accuracy and consistency, indicating its potential for advancing GR models in reasoning-intensive retrieval scenarios.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Dimensions overall score 7.0
PROBLEM
ReasonGR enhances generative retrieval models by improving multi-step semantic reasoning for complex numerical queries. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex queries in numerical contexts, such as those involving semantic...
METHOD
Generative retrieval (GR) models encode a corpus within model parameters and generate relevant document identifiers directly for a given query. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex queries in numerical contexts, such as t...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex queries in numerical contexts, such as those involving semantic reasoning over financial reports, due to limi...
WHY NOW
Generative Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
ReasonGR enhances generative retrieval models by improving multi-step semantic reasoning for complex numerical queries. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex queries in numerical contexts, such as those involving semantic reasoning over financial reports, due to limited reasoning capabilities.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Generative retrieval (GR) models encode a corpus within model parameters and generate relevant document identifiers directly for a given query. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex queries in numerical contexts, such as those involving semantic reasoning over financial reports, due to limited reasoning capabilities.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex queries in numerical contexts, such as those involving semantic reasoning over financial reports, due to limited reasoning capabilities.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Generative Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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ReasonGR enhances generative retrieval models by improving multi-step semantic reasoning for complex numerical queries.
Segment
Generative Retrieval
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Commercial read
7.0/10 public viability
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passport absent
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partial
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