RECOVER: Robust Entity Correction via agentic Orchestration of hypothesis Variants for Evidence-based Recovery

• URL: http://arxiv.org/abs/2603.16411v1
• Date: Tue, 17 Mar 2026 11:48:12 GMT
• Title: RECOVER: Robust Entity Correction via agentic Orchestration of hypothesis Variants for Evidence-based Recovery
• Abstract summary: We introduce RECOVER, an agentic correction framework that serves as a tool-using agent.<n>It leverages multiple hypotheses as evidence from ASR, retrieves relevant entities, and applies Large Language Model (LLM) correction under constraints.<n>It achieves 8-46% relative reductions in entity-phrase word error rate (E-WER) and increases recall by up to 22 percentage points.
• Score: 8.750432529472802
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Entity recognition in Automatic Speech Recognition (ASR) is challenging for rare and domain-specific terms. In domains such as finance, medicine, and air traffic control, these errors are costly. If the entities are entirely absent from the ASR output, post-ASR correction becomes difficult. To address this, we introduce RECOVER, an agentic correction framework that serves as a tool-using agent. It leverages multiple hypotheses as evidence from ASR, retrieves relevant entities, and applies Large Language Model (LLM) correction under constraints. The hypotheses are used using different strategies, namely, 1-Best, Entity-Aware Select, Recognizer Output Voting Error Reduction (ROVER) Ensemble, and LLM-Select. Evaluated across five diverse datasets, it achieves 8-46% relative reductions in entity-phrase word error rate (E-WER) and increases recall by up to 22 percentage points. The LLM-Select achieves the best overall performance in entity correction while maintaining overall WER.

Related papers

• A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination [51.06539604709775]
  We propose A-SR, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views.<n>A-SR coordinates formula discovery through routing among coordination protocols, an online evaluator-reward role policy, and state-routed process memory.
  arXiv  Detail & Related papers  (2026-08-05T14:01:10Z)
• SVR: Self-Verifying Refinement via Joint Verdict-Confidence Reinforcement Learning for Adaptive Test-Time Compute [62.3458279176813]
  Self-Verifying Refinement is an oracle-free multi-turn reinforcement learning framework.<n>It learns to use self-verification as a compute-control policy.<n>It achieves a macro-average accuracy of 0.563 with only 2.99 inference turns on average.
  arXiv  Detail & Related papers  (2026-07-30T16:20:58Z)
• Retrieval-Augmented Self-Taught Reasoning Model with Adaptive Chain-of-Thought for ASR Named Entity Correction [12.483998165719981]
  We propose a retrieval-augmented generation framework for correcting named entity errors in automatic speech recognition (ASR)<n>Our approach consists of two key components: (1) a rephrasing language model (RLM) for named entity recognition, followed by candidate retrieval using a phonetic-level edit distance; and (2) a novel self-taught reasoning model with adaptive chain-of-thought (A-STAR) that dynamically adjusts the depth of its reasoning based on task difficulty.
  arXiv  Detail & Related papers  (2026-01-21T15:05:39Z)
• CARE What Fails: Contrastive Anchored-REflection for Verifiable Multimodal [84.71254539482369]
  Group-relative reinforcement learning with verifiable rewards (RLVR) often wastes the most informative data it already has the failures.<n>We present CARE, a failure-centric post-training framework for multimodal reasoning that turns errors into supervision.<n> CARE improves accuracy and training smoothness while explicitly increasing the share of learning signal that comes from failures.
  arXiv  Detail & Related papers  (2025-12-22T16:34:21Z)
• Relational Deep Dive: Error-Aware Queries Over Unstructured Data [9.0236658372663]
  ReDD (Relational Deep Dive) is a framework that dynamically discovers query-specific schemas, populates relational tables, and ensures error-aware extraction with provable guarantees.<n>Main contribution is SCAPE, a statistically calibrated method for error detection with coverage guarantees, and SCAPE-HYB, a hybrid approach that optimize the trade-off between accuracy and human correction costs.
  arXiv  Detail & Related papers  (2025-11-04T16:30:55Z)
• Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models [28.75106676284909]
  We propose SelRepair, a novel APR approach with integration of a fine-tuned LLM with a newly-designed dual RAG module.<n>This approach uses a bug-fix pair dataset for fine-tuning and incorporates semantic and syntactic/structural similarity information through an RAG selection gate.<n> Evaluations on Java datasets show SelRepair outperforms other APR methods, achieving 26.29% and 17.64% in terms of exact match (EM) on different datasets while reducing inference time by at least 6.42% with controlled input lengths.
  arXiv  Detail & Related papers  (2025-07-14T09:41:51Z)
• Latent Veracity Inference for Identifying Errors in Stepwise Reasoning [78.29317733206643]
  We introduce Veracity Search (VS), a discrete search algorithm over veracity assignments.<n>It performs otherwise intractable inference in the posterior distribution over latent veracity values.<n>It generalizes VS, enabling accurate zero-shot veracity inference in novel contexts.
  arXiv  Detail & Related papers  (2025-05-17T04:16:36Z)
• Self-Routing RAG: Binding Selective Retrieval with Knowledge Verbalization [95.85537087475882]
  Existing approaches underutilize the inherent knowledge of large language models (LLMs)<n>We propose Self-Routing RAG, a novel framework that binds selective retrieval with knowledge verbalization.<n> SR-RAG reduces the number of retrievals by 29% while improving performance by 5.1%.
  arXiv  Detail & Related papers  (2025-04-01T17:59:30Z)
• Error Correction by Paying Attention to Both Acoustic and Confidence References for Automatic Speech Recognition [52.624909026294105]
  We propose a non-autoregressive speech error correction method. A Confidence Module measures the uncertainty of each word of the N-best ASR hypotheses. The proposed system reduces the error rate by 21% compared with the ASR model.
  arXiv  Detail & Related papers  (2024-06-29T17:56:28Z)
• Crossmodal ASR Error Correction with Discrete Speech Units [16.58209270191005]
  We propose a post-ASR processing approach for ASR Error Correction (AEC) We explore pre-training and fine-tuning strategies and uncover an ASR domain discrepancy phenomenon. We propose the incorporation of discrete speech units to align with and enhance the word embeddings for improving AEC quality.
  arXiv  Detail & Related papers  (2024-05-26T19:58:38Z)
• How Fragile is Relation Extraction under Entity Replacements? [70.34001923252711]
  Relation extraction (RE) aims to extract the relations between entity names from the textual context. Existing work has found that the RE models the entity name patterns to make RE predictions while ignoring the textual context. This motivates us to raise the question: are RE models robust to the entity replacements?''
  arXiv  Detail & Related papers  (2023-05-22T23:53:32Z)
• Factual Error Correction for Abstractive Summaries Using Entity Retrieval [57.01193722520597]
  We propose an efficient factual error correction system RFEC based on entities retrieval post-editing process. RFEC retrieves the evidence sentences from the original document by comparing the sentences with the target summary. Next, RFEC detects the entity-level errors in the summaries by considering the evidence sentences and substitutes the wrong entities with the accurate entities from the evidence sentences.
  arXiv  Detail & Related papers  (2022-04-18T11:35:02Z)
• Robust Spoken Language Understanding with RL-based Value Error Recovery [35.82890898452309]
  Spoken Language Understanding (SLU) aims to extract structured semantic representations (e.g., slot-value pairs) from speech recognized texts. We propose a new robust SLU framework to guide the SLU input adaptation with a rule-based value error recovery module. Experiments on the public CATSLU dataset show the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2020-09-07T13:32:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.