Robust Dual-Signal Fusion: Hybrid Neuro-Symbolic Gating with Compressed Chain-of-Thought Refinement for Irony Detection in Social Media Texts

• URL: http://arxiv.org/abs/2606.16845v2
• Date: Tue, 23 Jun 2026 10:44:51 GMT
• Title: Robust Dual-Signal Fusion: Hybrid Neuro-Symbolic Gating with Compressed Chain-of-Thought Refinement for Irony Detection in Social Media Texts
• Abstract summary: We introduce the Robust Dual-Signal (RDS) Fusion, a neuro-symbolic architecture that compresses Chain-of-Thought (CoT) reasoning trajectories without Supervised Fine-Tuning (SFT)<n>RDS achieves $78.1%$ accuracy and a Macro F1 of $0.777$, matching the absolute performance ceiling of a fine-tuned BERTweet.<n> RDS fusion is statistically insignificant compared to the combined RoBERTa ablation; the concurrent synergy achieves a statistically significant improvement over the standalone baseline.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Small-scale Large Language Models (LLMs) natively default to literal semantic interpretations, making few-shot irony detection a persistent challenge in noisy, user-generated text. We introduce the Robust Dual-Signal (RDS) Fusion framework, a hybrid neuro-symbolic architecture that compresses Chain-of-Thought (CoT) reasoning trajectories without Supervised Fine-Tuning (SFT). Evaluated on a strictly held-out TweetEval test set ($N=734$), RDS achieves $78.1\%$ accuracy and a Macro F1 of $0.777$, matching the absolute performance ceiling of a fine-tuned BERTweet. On the heavily imbalanced iSarcasm dataset, the frozen CoT pipeline filters $22.5\%$ of out-of-distribution hallucinations, yielding a few-shot Macro F1 of $0.6726$ and Ironic F1 of $0.4821$, outperforming multiple heavily supervised SemEval transformer ensembles. Statistical ablation confirms this structural synergy: while adding the symbolic prior to the neural baseline yields an insignificant gain, and the RDS fusion is statistically insignificant compared to the combined RoBERTa and symbolic prior ablation; the concurrent fusion achieves a statistically significant improvement over the standalone baseline ($p=0.005$).

Related papers

• SANE: State Anomaly Neutralization for Stable Extreme-Context Delta-Rule Models [53.86918766240095]
  Delta-Rule recurrent models maintain a fixed-size state, enabling $O(1)$ inference memory but potentially becoming unstable under extreme-context extrapolation.<n>We propose textbfState Anomaly Neutralization (SANE), which applies adaptive $tanh$ compression at chunk boundaries while preserving the intra-chunk parallel structure.
  arXiv  Detail & Related papers  (2026-08-23T10:41:07Z)
• Generative Criticality in Large Language Model Temperature Scaling [3.472734054649125]
  We propose a statistical-field framework for text generated by large language models (LLMs)<n>We define a susceptibility from the connected two-point correlator and an order parameter from the ensemble-averaged embedding field.<n>We observe a sharp susceptibility peak near a characteristic $T_c$ with power-law-like scaling, a concurrent rapid change in the order parameter, and a collapse onto a single semantic direction below $T_c$.
  arXiv  Detail & Related papers  (2026-06-04T14:43:32Z)
• Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline [43.0484058393522]
  We test a hybrid neural-symbolic pipeline against direct generation.<n>We evaluate on a 2,000-note synthetic outpatient corpus with action-disjoint splits.
  arXiv  Detail & Related papers  (2026-05-26T05:14:33Z)
• Amplifying, Not Learning: Fine-Tuned AI Text Detectors Amplify a Pretrained Direction [51.56484100374058]
  Text detectors amplify a pretrained typicality axis; they do not construct an AI-vs-human boundary.<n>On raw encoders before any task supervision, projecting onto centroid(AI)-centroid(HC3) achieves NYT-vs-HC3 AUROC 0.806/0.944/0.834 across three architectures.<n>On RoBERTa-base, raw projection exceeds fine-tuning; on RoBERTa-base, full fine-tuning reduces discrimination below raw on both fluent-formal populations tested.
  arXiv  Detail & Related papers  (2026-05-20T19:08:38Z)
• A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction [7.356849324535381]
  This study introduces yvsoucom-iterkit, a deterministic and log-driven automated machine learning framework.<n>Experiments on the Pima Indians Diabetes and Stroke datasets across more than 18,000 pipeline configurations reveal a partially redundant search space.<n>The framework achieves strong and stable performance using ensemble models.
  arXiv  Detail & Related papers  (2026-05-19T16:57:56Z)
• High-Probability Convergence in Decentralized Stochastic Optimization with Gradient Tracking [69.90407799170687]
  We study high-probability (HP) convergence guarantees in decentralized optimization.<n>Our results establish the same conditions on the horizon as in achieving comparable times.
  arXiv  Detail & Related papers  (2026-04-30T22:45:21Z)
• SHIFT: Robust Double Machine Learning for Average Dose-Response Functions under Heavy-Tailed Contamination [0.0]
  SHIFT (Self-calibrated Heavy-tail Inlier-Fit with Tempering) is a robust DML estimator.<n>It combines cross-fit nuisanceization with a kernel-local Welsch-loss second stage optimized by Graduated Non-Convexity.<n>It has competitive worst-case shape recovery across 1,400 mainsweep fits.
  arXiv  Detail & Related papers  (2026-04-30T19:51:35Z)
• Why Training-Free Token Reduction Collapses: The Inherent Instability of Pairwise Scoring Signals [0.0]
  Training-free token reduction methods for Vision Transformers employ different scoring mechanisms, yet they share a closely matched cliff-like collapse at high compression.<n>We develop a diagnostic framework with two tools, ranking consistency $_s$ and off-diagonal correlation $_textoff$, that decomposes the collapse into (1)a signal-agnostic error amplifier inherent to layer-wise reduction.<n>We construct CATIS as a constructive validation: unary signals raise the trigger threshold, triage suppresses the gain.
  arXiv  Detail & Related papers  (2026-04-17T23:26:27Z)
• Architecture-Agnostic Modality-Isolated Gated Fusion for Robust Multi-Modal Prostate MRI Segmentation [0.0]
  Multi-weighted prostate MRI combines T2-parametric (T2W), apparent diffusion coefficient (ADC), and high b-value diffusion-weighted (HBV) sequences.<n>In practice, the diffusion sequences are more frequently subject to acquisition variability, motion, and artifacts than T2W.<n>We propose Modality-Isolated Gated Fusion (MIGF) to maintain separate modality-specific encoding streams before a learned gating stage.
  arXiv  Detail & Related papers  (2026-04-12T15:54:21Z)
• Tight Convergence Rates for Online Distributed Linear Estimation with Adversarial Measurements [66.94250413799232]
  We study mean estimation of a random vector $X$ in a distributed parameter-server-worker setup.<n>Key challenges are adversarial measurements and asynchrony.<n>Results provide a unified finite-time characterization of robustness, identifiability, and statistical efficiency in distributed linear estimation with adversarial workers.
  arXiv  Detail & Related papers  (2026-04-07T11:45:55Z)
• Low-Rank Adaptation Reduces Catastrophic Forgetting in Sequential Transformer Encoder Fine-Tuning: Controlled Empirical Evidence and Frozen-Backbone Representation Probes [3.305265383862785]
  We present an empirical study of Low-Rank Adaptation (LoRA) in sequential transformer encoder finetuning with companion representation probes.<n>In five full-validation BERT-base reruns on an RTE->MRPC->CoLA->SST-2 sequence, full fine-tuning yields 19.9% +/- average forgetting, whereas standard LoRA (r, query/value modules) yields 0.6% +/-1.4%.
  arXiv  Detail & Related papers  (2026-03-29T14:14:36Z)
• Do Generative Metrics Predict YOLO Performance? An Evaluation Across Models, Augmentation Ratios, and Dataset Complexity [43.338311770275745]
  We present a controlled evaluation of synthetic augmentation for YOLOv11 across three single-class detection regimes.<n>We benchmark six GAN-, diffusion-, and hybrid-based generators over augmentation ratios from 10% to 150% of the real training split.<n>For each dataset-generator-augmentation configuration, we compute pre-training dataset metrics under a matched-size bootstrap protocol.
  arXiv  Detail & Related papers  (2026-02-20T03:02:36Z)
• Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression [55.63153956934198]
  Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs)<n>Existing CoT compression methods often suffer from a critical loss of logical fidelity at high compression ratios.<n>We propose a novel EXTreme-RAtio Chain-of-Thought Compression framework, termed Extra-CoT, which aggressively reduces the token budget while preserving answer accuracy.
  arXiv  Detail & Related papers  (2026-02-09T06:57:15Z)
• Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models [45.21399037022976]
  CausalMixFT is a method that enhances fine-tuning robustness and downstream performance.<n>It generates structurally consistent synthetic samples using Structural Causal Models (SCMs) fitted on the target dataset.<n> evaluated across 33 classification datasets from TabArena and over 2300 fine-tuning runs.
  arXiv  Detail & Related papers  (2026-01-07T17:16:39Z)
• A Two-Stage Strategy for Mitosis Detection Using Improved YOLO11x Proposals and ConvNeXt Classification [5.1547008655164195]
  MIDOG 2025 Track 1 requires mitosis detection in whole-slideimages (WSIs) containing non-tumor, inflamed, and necrotic re-gions.<n>Due to the complicated and heterogeneous context, there are often false positives and falsenegatives, thus degrading the detection F1-score.<n>We propose a two-stage framework to generate mitosis candidates and filter out false positives.<n>On the MIDOG 2025 Track 1 preliminary test set, thealgorithm scores an F1 score of 0.7587.
  arXiv  Detail & Related papers  (2025-09-01T15:46:28Z)
• Constructive Universal Approximation and Sure Convergence for Multi-Layer Neural Networks [0.0]
  o1Neuro is a new neural network model built on sparse indicator activation neurons.<n>At the population level, a deep o1Neuro can approximate any measurable function of $boldsymbolX$.<n>At the sample level, the optimization of o1Neuro reaches an optimal model with probability approaching one after sufficiently many update rounds.
  arXiv  Detail & Related papers  (2025-07-07T08:55:28Z)
• Inertial Confinement Fusion Forecasting via Large Language Models [48.76222320245404]
  In this study, we introduce $textbfLPI-LLM$, a novel integration of Large Language Models (LLMs) with classical reservoir computing paradigms. We propose the $textitLLM-anchored Reservoir$, augmented with a $textitFusion-specific Prompt$, enabling accurate forecasting of $textttLPI$-generated-hot electron dynamics during implosion. We also present $textbfLPI4AI$, the first $textttLPI$ benchmark based
  arXiv  Detail & Related papers  (2024-07-15T05:46:44Z)
• MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers [41.56951365163419]
  "MixedNUTS" is a training-free method where the output logits of a robust classifier are processed by nonlinear transformations with only three parameters. MixedNUTS then converts the transformed logits into probabilities and mixes them as the overall output. On CIFAR-10, CIFAR-100, and ImageNet datasets, experimental results with custom strong adaptive attacks demonstrate MixedNUTS's vastly improved accuracy and near-SOTA robustness.
  arXiv  Detail & Related papers  (2024-02-03T21:12:36Z)
• DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning [52.83811558753284]
  Decentralized learning (DFL) discards the central server and establishes a decentralized communication network. Existing DFL methods still suffer from two major challenges: local inconsistency and local overfitting.
  arXiv  Detail & Related papers  (2023-08-16T11:22:36Z)
• Permutation Compressors for Provably Faster Distributed Nonconvex Optimization [68.8204255655161]
  We show that the MARINA method of Gorbunov et al (2021) can be considered as a state-of-the-art method in terms of theoretical communication complexity. Theory of MARINA to support the theory of potentially em correlated compressors, extends to the method beyond the classical independent compressors setting.
  arXiv  Detail & Related papers  (2021-10-07T09:38:15Z)

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.