Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction

• URL: http://arxiv.org/abs/2605.20194v1
• Date: Sat, 04 Apr 2026 05:11:20 GMT
• Title: Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction
• Abstract summary: This study proposes a structured framework combining parallel chunk-level processing with evidence-anchored consolidation.<n>Experiments with multiple model types and sizes indicate that parallel processing significantly reduces omission error by approximately 84%.<n>Smaller models benefited most, suggesting that efficient parallel chunking and consolidation play a critical role in achieving reliable and scalable textual analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have been increasingly used to analyze text. However, they are often plagued with contextual reasoning limitations when analyzing long documents. When long documents are processed sequentially, early or dominant concepts can overshadow less visible but meaningful interpretations, leading to cumulative analytical bias, omission error, and over-generalization. Additionally, independently generated outputs are often merged without systematic grounding, introducing redundancy, conceptual drift, and unsupported claims. This study proposes a structured framework combining parallel chunk-level processing with evidence-anchored consolidation. Texts are first divided into semantically coherent chunks and processed independently in parallel to remove influence from earlier processing. The independently generated interpretations are then consolidated using explicit evidence anchoring and prioritization that reduces dominance and over-generalization while improving traceability. Experiments with multiple model types and sizes indicate that parallel processing significantly reduces omission error by approximately 84%, increases evidence traceability by up to 130%, and reduces unsupported claims by up to 91%. Smaller models benefited most, suggesting that efficient parallel chunking and consolidation play a critical role in achieving reliable and scalable textual analysis.

Related papers

• Buried in Textual Debt: Context Pruning with Visual Evidence Preservation for MLLM Agents [16.82888862043366]
  We propose a Kullback-Leibler-guided framework for pruning accumulated reasoning in tool-use agents.<n> SPARE achieves the highest average accuracy among pruning methods while removing 37.89-64.58% of reasoning tokens.<n>This favorable accuracy-context trade-off shows that reducing textual dominance restores reliance on visual evidence and mitigates over-conditioning on self-generated language.
  arXiv  Detail & Related papers  (2026-08-24T08:29:17Z)
• Combining Trained Models in Reinforcement Learning [0.0]
  Deep reinforcement learning (DRL) has delivered strong results in domains such as Atari and Go, but it still suffers from high sample cost and weak transfer beyond the training setting.<n>A common response is to reuse information from previously trained models through transfer, distillation, ensemble methods, or federated training.<n>This paper presents a PRISMA-guided systematic review of empirical studies on pretrained knowledge reuse in DRL.
  arXiv  Detail & Related papers  (2026-05-04T02:37:55Z)
• PLUME: Latent Reasoning Based Universal Multimodal Embedding [52.35354073629127]
  Universal multimodal embedding (UME) maps heterogeneous inputs into a shared retrieval space with a single model.<n>Recent approaches improve UME by generating explicit chain-of-thought (CoT) rationales before extracting embeddings.<n>We propose PLUME, a latent reasoning framework that advances UME by replacing verbalized CoT with a short autoregressive rollout of continuous latent states.
  arXiv  Detail & Related papers  (2026-04-02T14:04:53Z)
• Diagnosing Structural Failures in LLM-Based Evidence Extraction for Meta-Analysis [0.8193467416247519]
  Review and meta-analyses rely on converting narrative articles into structured, numerically grounded study records.<n>Despite rapid advances in large language models (LLMs), it remains unclear whether they can meet the structural requirements of this process.<n>We propose a structural, diagnostic framework that evaluates LLM-based evidence extraction as a progression of schema-constrained queries.
  arXiv  Detail & Related papers  (2026-02-11T14:09:43Z)
• Faithful Summarisation under Disagreement via Belief-Level Aggregation [10.334277776439423]
  We introduce a disagreement-aware synthesis pipeline that separates belief-level aggregation from language generation.<n>Our results show that while sufficiently large models can match belief-level aggregation when aggregation is handled at generation time, this behaviour is not stable across architectures or capacities.<n>In contrast, belief-level aggregation combined with simple prompting yields consistently strong disagreement-aware performance across models.
  arXiv  Detail & Related papers  (2026-01-08T12:40:47Z)
• Resolving Evidence Sparsity: Agentic Context Engineering for Long-Document Understanding [49.26132236798123]
  Vision Language Models (VLMs) have gradually become a primary approach in document understanding.<n>We propose SLEUTH, a multi agent framework that orchestrates a retriever and four collaborative agents in a coarse to fine process.<n>The framework identifies key textual and visual clues within the retrieved pages, filters for salient visual evidence such as tables and charts, and analyzes the query to devise a reasoning strategy.
  arXiv  Detail & Related papers  (2025-11-28T03:09:40Z)
• Compose and Fuse: Revisiting the Foundational Bottlenecks in Multimodal Reasoning [49.17801010041155]
  Multimodal large language models (MLLMs) promise enhanced reasoning by integrating diverse inputs such as text, vision, and audio.<n>Yet cross-modal reasoning remains underexplored, with conflicting reports on whether added modalities help or harm performance.<n>We categorize multimodal reasoning into six interaction patterns, varying how facts are distributed across modalities and logically combined.
  arXiv  Detail & Related papers  (2025-09-28T08:46:11Z)
• Context-Aware Hierarchical Merging for Long Document Summarization [56.96619074316232]
  We propose different approaches to enrich hierarchical merging with context from the source document.<n> Experimental results on datasets representing legal and narrative domains show that contextual augmentation consistently outperforms zero-shot and hierarchical merging baselines.
  arXiv  Detail & Related papers  (2025-02-03T01:14:31Z)
• LLM$\ imes$MapReduce: Simplified Long-Sequence Processing using Large Language Models [73.13933847198395]
  We propose a training-free framework for processing long texts, utilizing a divide-and-conquer strategy to achieve comprehensive document understanding. The proposed LLM$times$MapReduce framework splits the entire document into several chunks for LLMs to read and then aggregates the intermediate answers to produce the final output.
  arXiv  Detail & Related papers  (2024-10-12T03:13:44Z)
• 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)

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.