Optimizing What We Trust: Reliability-Guided QUBO Selection of Multi-Agent Weak Framing Signals for Arabic Sentiment Prediction

• URL: http://arxiv.org/abs/2603.04416v1
• Date: Wed, 04 Feb 2026 09:19:43 GMT
• Title: Optimizing What We Trust: Reliability-Guided QUBO Selection of Multi-Agent Weak Framing Signals for Arabic Sentiment Prediction
• Abstract summary: Framing detection in Arabic social media is difficult due to interpretive ambiguity, cultural grounding, and limited reliable supervision.<n>We propose a reliability-aware weak supervision framework that shifts the focus from label fusion to data curation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Framing detection in Arabic social media is difficult due to interpretive ambiguity, cultural grounding, and limited reliable supervision. Existing LLM-based weak supervision methods typically rely on label aggregation, which is brittle when annotations are few and socially dependent. We propose a reliability-aware weak supervision framework that shifts the focus from label fusion to data curation. A small multi-agent LLM pipeline, two framers, a critic, and a discriminator, treats disagreement and reasoning quality as epistemic signals and produces instance-level reliability estimates. These estimates guide a QUBO-based subset selection procedure that enforces frame balance while reducing redundancy. Intrinsic diagnostics and an out-of-domain Arabic sentiment transfer test show that the selected subsets are more reliable and encode non-random, transferable structure, without degrading strong text-only baselines.

Related papers

• Tracing Provenance and Detecting Tampering with Complementary LLM Watermarks [68.64050157343334]
  Existing watermarks preserve provenance under editing, but this same robustness allows an adversary to alter critical content while retaining attribution.<n>We introduce an innovative watermark that jointly provides provenance and tamper evidence.<n>The signals share the same mechanism but use independent keys and different seeding windows over normalized text, making one resilient to edits and the other sensitive to reader-visible changes.
  arXiv  Detail & Related papers  (2026-08-13T01:55:16Z)
• Inference-Time Steering for Cross-Lingual Factual Consistency in LLMs [51.56484100374058]
  Large Language Models (LLMs) demonstrate remarkable multilingual fluency, but their internal knowledge representations remain disproportionately biased toward high-resource languages.<n>We investigate whether these biases can be mitigated at inference time, forcing an English-prompted model to answer as if it were queried in target languages.<n>Experiments on Gemma 3 12B Instruct reveal persona prompting to be the strongest overall intervention, balancing efficacy, safety, and out-of-domain generalization.
  arXiv  Detail & Related papers  (2026-07-21T16:15:05Z)
• AI Agents Do Not Fail Alone:The Context Fails First [0.0]
  This paper validates context-engineering quality as an independent leading indicator of agent reliability.<n>We implement the measurement in ProofAgent-Harness, an open-source infrastructure for AI agent evaluation.<n>We show that context-quality criteria consistently predict their corresponding behavioral outcomes.
  arXiv  Detail & Related papers  (2026-07-15T18:33:02Z)
• Robust Checkpoint Selection for Multimodal LLMs via Agentic Evaluation and Stability-Aware Ranking [0.4562811934651716]
  We formulate checkpoint selection as a robust decision problem under evaluation uncertainty.<n>We propose a multi-stage framework that integrates curated real-world data, structured LLM-based judgment, and multi-stage ranking protocols.<n>We demonstrate that data quality, specifically OCR readability, is a critical determinant of evaluation validity.
  arXiv  Detail & Related papers  (2026-05-13T12:18:32Z)
• Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity [70.60312607268871]
  FedQual is a quality-aware Fed-LDL framework with two mechanisms.<n>We construct four new Fed-LDL benchmarks with controlled annotation quality disparity.
  arXiv  Detail & Related papers  (2026-05-06T12:20:20Z)
• MADE: A Living Benchmark for Multi-Label Text Classification with Uncertainty Quantification of Medical Device Adverse Events [0.7685926099671206]
  Machine learning in high-stakes domains such as healthcare requires strong predictive performance and reliable uncertainty quantification.<n>We introduce MADE, a living MLTC benchmark derived from medical device adverse event reports.<n>We systematically assess entropy-/consistency-based and self-verbalized UQ methods.
  arXiv  Detail & Related papers  (2026-04-16T16:28:16Z)
• VIOLA: Towards Video In-Context Learning with Minimal Annotations [20.810620293371027]
  We introduce VIOLA, a framework that synergizes minimal expert supervision with abundant unlabeled data.<n>Our framework significantly outperforms various baselines in low-resource settings, achieving robust adaptation with minimal annotation costs.
  arXiv  Detail & Related papers  (2026-01-22T00:35:30Z)
• All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection [67.89888669159899]
  RFC Bench is a benchmark for evaluating large language models on financial misinformation under realistic news.<n>The benchmark defines two complementary tasks: reference free misinformation detection and comparison based diagnosis.
  arXiv  Detail & Related papers  (2026-01-07T18:18:28Z)
• Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection [76.91230292971115]
  Large language model (LLM)-based multi-agent systems (MAS) have shown strong capabilities in solving complex tasks.<n>XG-Guard is an explainable and fine-grained safeguarding framework for detecting malicious agents in MAS.
  arXiv  Detail & Related papers  (2025-12-21T13:46:36Z)
• Rubric-Conditioned LLM Grading: Alignment, Uncertainty, and Robustness [4.129847064263056]
  We systematically evaluate the performance of Large Language Models for rubric-based short-answer grading.<n>We find that alignment is strong for binary tasks but degrades with increased rubric granularity.<n>Experiments reveal that while the model is resilient to prompt injection, it is sensitive to synonym substitutions.
  arXiv  Detail & Related papers  (2025-12-21T05:22:04Z)
• Self-Filtered Distillation with LLMs-generated Trust Indicators for Reliable Patent Classification [29.336193842747832]
  We introduce Self-Filtered Distillation, a framework specifically tailored for patent classification.<n>It treats LLM-generated rationales as trust signals rather than ground-truth supervision.<n>Our method outperforms label-based learning and conventional distillation in accuracy, stability, and interpretability.
  arXiv  Detail & Related papers  (2025-10-06T22:50:01Z)
• An Ensemble Framework for Unbiased Language Model Watermarking [60.99969104552168]
  We propose ENS, a novel ensemble framework that enhances the detectability and robustness of unbiased watermarks.<n>ENS sequentially composes multiple independent watermark instances, each governed by a distinct key, to amplify the watermark signal.<n> Empirical evaluations show that ENS substantially reduces the number of tokens needed for reliable detection and increases resistance to smoothing and paraphrasing attacks.
  arXiv  Detail & Related papers  (2025-09-28T19:37:44Z)
• MARBLE: A Multi-Agent Rule-Based LLM Reasoning Engine for Accident Severity Prediction [1.3102025155414727]
  Accident severity prediction plays a critical role in transportation safety systems.<n>Existing methods often rely on monolithic models or black box prompting.<n>We propose a multiagent rule based LLM engine that decomposes the severity prediction task across a team of specialized reasoning agents.
  arXiv  Detail & Related papers  (2025-07-07T11:27:49Z)
• TrustLoRA: Low-Rank Adaptation for Failure Detection under Out-of-distribution Data [62.22804234013273]
  We propose a simple failure detection framework to unify and facilitate classification with rejection under both covariate and semantic shifts.<n>Our key insight is that by separating and consolidating failure-specific reliability knowledge with low-rank adapters, we can enhance the failure detection ability effectively and flexibly.
  arXiv  Detail & Related papers  (2025-04-20T09:20:55Z)

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.