Related papers: A Study on Differentiable Logic and LLMs for EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2023

Related papers

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning [57.16513298507272]
We propose IADD-TR, a unified framework combining Intervention-Aware Dynamics Decoupling (IADD) and Targeted Regularization (TR)<n>IADD factorizes transitions into an action-intervention stage and an action-free natural evolution stage, using a zero-action anchor to resolve the non-uniqueness of this two-stage factorization for robust generalization.<n>For policy learning, we derive TR from the efficient influence function of a replay-state policy-gradient functional.
arXiv Detail & Related papers (2026-08-11T08:20:02Z)
The Art of Interrogation: Consistency Amplifies Factuality in Spatial Reasoning [65.67266333751569]
Current Large Reasoning Models (LRMs) exhibit remarkable general capabilities but significantly underperform in spatial reasoning tasks.<n>We propose a self-supervised reinforcement learning framework that targets the internal reasoning process without requiring ground-truth annotations.<n>We show that this label-free consistency training approaches the accuracy of models trained with ground-truth supervision and achieves similar generalization across diverse tasks and data domains.
arXiv Detail & Related papers (2026-06-10T10:50:06Z)
Metacognition as Reward: Reinforcing LLM Reasoning via Knowledge and Regulation Signals [75.25256166997414]
We introduce Metacognition-as-Reward (MaR), a metacognition-inspired RL framework.<n>MaR guides reasoning through two general process dimensions.<n>MaR consistently improves model performance, achieving up to a 7.7% gain over the base model.
arXiv Detail & Related papers (2026-05-22T08:54:37Z)
Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models [46.26628756478016]
Large Language Models (LLMs) are known to acquire reasoning capabilities through shared inference patterns in pre-training data.<n>We show that reasoning conflicts are internally detectable, as confidence scores significantly drop during conflicting episodes.<n>Our findings establish that while LLM reasoning is anchored to concrete instances, active mechanistic interventions can effectively decouple logical schemata from data.
arXiv Detail & Related papers (2026-04-29T22:55:40Z)
Neuro-Symbolic Learning for Predictive Process Monitoring via Two-Stage Logic Tensor Networks with Rule Pruning [1.554831836850549]
Existing data-driven approaches fail to incorporate domain-specific sequential constraints and logical rules governing event relationships.<n>We present a neuro-symbolic approach integrating domain knowledge as differentiable logical constraints using Logic Networks (LTNs)<n>We formalize control-flow, temporal, and payload knowledge using Linear Temporal Logic and first-order logic.
arXiv Detail & Related papers (2026-03-27T19:32:49Z)
Interaction-Grounded Learning for Contextual Markov Decision Processes with Personalized Feedback [59.287761696290865]
We propose a computationally efficient algorithm that achieves a sublinear regret guarantee for contextual episodic Markov Decision Processes (MDPs) with personalized feedback.<n>We demonstrate the effectiveness of our method in learning personalized objectives from multi-turn interactions through experiments on both a synthetic episodic MDP and a real-world user booking dataset.
arXiv Detail & Related papers (2026-02-09T06:29:54Z)
Reshaping Action Error Distributions for Reliable Vision-Language-Action Models [69.38615670891038]
In robotic manipulation, vision-language-action (VLA) models have emerged as a promising paradigm for learning generalizable and scalable robot policies.<n>We focus on continuous-action VLA models and move beyond conventional MSE-based regression by reshaping action error distributions during training.<n>We evaluate our approaches across standard, few-shot, and noisy settings on multiple representative VLA architectures.
arXiv Detail & Related papers (2026-02-04T05:37:09Z)
Identifying and Transferring Reasoning-Critical Neurons: Improving LLM Inference Reliability via Activation Steering [50.63386303357225]
We propose AdaRAS, a lightweight test-time framework that improves reasoning reliability by selectively intervening on neuron activations.<n>AdaRAS identifies Reasoning-Critical Neurons (RCNs) via a polarity-aware mean-difference criterion and adaptively steers their activations during inference.<n> Experiments on 10 mathematics and coding benchmarks demonstrate consistent improvements, including over 13% gains on AIME-24 and AIME-25.
arXiv Detail & Related papers (2026-01-27T17:53:01Z)
Beyond Outcome Verification: Verifiable Process Reward Models for Structured Reasoning [14.632557283678898]
Verifiable Process Reward Models (VPRMs) are a reinforcement-learning framework in which intermediate reasoning steps are checked by deterministic, rule-based verifiers.<n>We apply VPRMs to risk-of-bias assessment for medical evidence synthesis.<n>Results show that VPRMs achieve up to 20% higher F1 than state-of-the-art models and 6.5% higher than verifiable outcome rewards.
arXiv Detail & Related papers (2026-01-23T23:22:20Z)
From Efficiency to Adaptivity: A Deeper Look at Adaptive Reasoning in Large Language Models [17.96350700093472]
Current large language models (LLMs) apply uniform reasoning strategies regardless of task complexity, generating long traces for trivial problems while failing to extend reasoning for difficult tasks.<n>This survey reframes reasoning through the lens of adaptivity: the capability to allocate reasoning effort based on input characteristics such as difficulty and uncertainty.
arXiv Detail & Related papers (2025-11-13T20:25:04Z)
Latent Chain-of-Thought for Visual Reasoning [53.541579327424046]
Chain-of-thought (CoT) reasoning is critical for improving the interpretability and reliability of Large Vision-Language Models (LVLMs)<n>We reformulate reasoning in LVLMs as posterior inference and propose a scalable training algorithm based on amortized variational inference.<n>We empirically demonstrate that the proposed method enhances the state-of-the-art LVLMs on seven reasoning benchmarks.
arXiv Detail & Related papers (2025-10-27T23:10:06Z)
Fault Cause Identification across Manufacturing Lines through Ontology-Guided and Process-Aware FMEA Graph Learning with LLMs [1.9563024477582351]
This study proposes a process-aware framework that enhances FMEA reusability by combining manufacturing-domain conceptualization with graph neural network (GNN) reasoning.<n>A case study on automotive pressure sensor assembly lines demonstrates that the proposed method outperforms a state-of-the-art retrieval-augmented generation (RAG) baseline.
arXiv Detail & Related papers (2025-10-17T08:35:47Z)
A Study of Rule Omission in Raven's Progressive Matrices [0.0]
Analogical reasoning lies at the core of human cognition and remains a fundamental challenge for artificial intelligence.<n>This study investigates the generalization capacity of modern AI systems under conditions of incomplete training.<n>Experiments reveal that although transformers demonstrate strong performance on familiar rules, their accuracy declines sharply when faced with novel or omitted rules.
arXiv Detail & Related papers (2025-10-03T15:53:28Z)
Audited Reasoning Refinement: Fine-Tuning Language Models via LLM-Guided Step-Wise Evaluation and Correction [1.41282143488996]
Training a task-specific small reasoning model is challenging when direct human supervision or high-quality labels are scarce.<n>We propose Reason-Refine-then-Align (R2tA), which turns refined model rationales into supervision for training task-specific reasoning models.
arXiv Detail & Related papers (2025-09-15T21:47:52Z)
Zero-Shot Anomaly Detection with Dual-Branch Prompt Learning [17.263625932911534]
Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories.<n>Existing ZSAD methods, whether using fixed or learned prompts, struggle under domain shifts because their training data are derived from limited training domains.<n>We introduce PILOT, a framework designed to overcome these challenges through two key innovations.
arXiv Detail & Related papers (2025-08-01T17:00:12Z)
A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search [15.387256204743407]
Large language models (LLMs) demand considerable computational, energy, and financial resources during both training and deployment.<n>Inference costs now represent a significant and growing component of the overall resource burden.<n>We introduce directed skill search (DS3), a general framework that represents inference as expressive over a learned skill graph.
arXiv Detail & Related papers (2025-06-10T14:47:48Z)
Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments [5.5855749614100825]
This paper addresses the hypothesis that leveraging multiple pre-trained models can mitigate this recall reduction.<n>We formulate the challenge of identifying and managing conflicting predictions from various models as a consistency-based abduction problem.<n>Our results validate the use of consistency-based abduction as an effective mechanism to robustly integrate knowledge from multiple imperfect models in challenging, novel scenarios.
arXiv Detail & Related papers (2025-05-25T23:17:47Z)
Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric [99.56567010306807]
Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications.<n>One core challenge of evaluation in the large language model (LLM) era is the generalization issue.<n>We propose Model Utilization Index (MUI), a mechanism interpretability enhanced metric that complements traditional performance scores.
arXiv Detail & Related papers (2025-04-10T04:09:47Z)
Teaching LLMs According to Their Aptitude: Adaptive Reasoning for Mathematical Problem Solving [55.895917967408586]
Existing approaches to mathematical reasoning with large language models rely on Chain-of-Thought (CoT) for generalizability or Tool-Integrated Reasoning (TIR) for precise computation. We propose TATA (Teaching LLMs According to Their Aptitude), an adaptive framework that enables LLMs to personalize their reasoning strategy spontaneously.
arXiv Detail & Related papers (2025-02-17T16:56:23Z)
PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings [55.55445978692678]
PseudoNeg-MAE is a self-supervised learning framework that enhances global feature representation of point cloud mask autoencoders. We show that PseudoNeg-MAE achieves state-of-the-art performance on the ModelNet40 and ScanObjectNN datasets.
arXiv Detail & Related papers (2024-09-24T07:57:21Z)
Making Large Language Models Better Planners with Reasoning-Decision Alignment [70.5381163219608]
We motivate an end-to-end decision-making model based on multimodality-augmented LLM. We propose a reasoning-decision alignment constraint between the paired CoTs and planning results. We dub our proposed large language planners with reasoning-decision alignment as RDA-Driver.
arXiv Detail & Related papers (2024-08-25T16:43:47Z)
Supervised Gradual Machine Learning for Aspect Category Detection [0.9857683394266679]
Aspect Category Detection (ACD) aims to identify implicit and explicit aspects in a given review sentence. We propose a novel approach to tackle the ACD task by combining Deep Neural Networks (DNNs) with Gradual Machine Learning (GML) in a supervised setting.
arXiv Detail & Related papers (2024-04-08T07:21:46Z)
Latent Alignment with Deep Set EEG Decoders [44.128689862889715]
We introduce the Latent Alignment method that won the Benchmarks for EEG Transfer Learning competition. We present its formulation as a deep set applied on the set of trials from a given subject. Our experimental results show that performing statistical distribution alignment at later stages in a deep learning model is beneficial to the classification accuracy.
arXiv Detail & Related papers (2023-11-29T12:40:45Z)
Domain Adaptation with Adversarial Training on Penultimate Activations [82.9977759320565]
Enhancing model prediction confidence on unlabeled target data is an important objective in Unsupervised Domain Adaptation (UDA) We show that this strategy is more efficient and better correlated with the objective of boosting prediction confidence than adversarial training on input images or intermediate features.
arXiv Detail & Related papers (2022-08-26T19:50:46Z)
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation [122.4588401267544]
General Scene Graph Generation (SGG) models tend to predict head predicates and re-balancing strategies prefer tail categories. We propose an Adaptive Fine-Grained Predicates Learning (FGPL-A) which aims at differentiating hard-to-distinguish predicates for SGG. Our proposed model-agnostic strategy significantly boosts performance of benchmark models on VG-SGG and GQA-SGG datasets by up to 175% and 76% on Mean Recall@100, achieving new state-of-the-art performance.
arXiv Detail & Related papers (2022-07-11T03:37:57Z)
LogiGAN: Learning Logical Reasoning via Adversarial Pre-training [58.11043285534766]
We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models. Inspired by the facilitation effect of reflective thinking in human learning, we simulate the learning-thinking process with an adversarial Generator-Verifier architecture. Both base and large size language models pre-trained with LogiGAN demonstrate obvious performance improvement on 12 datasets.
arXiv Detail & Related papers (2022-05-18T08:46:49Z)
Algorithms and Theory for Supervised Gradual Domain Adaptation [19.42476993856205]
We study the problem of supervised gradual domain adaptation, where labeled data from shifting distributions are available to the learner along the trajectory. Under this setting, we provide the first generalization upper bound on the learning error under mild assumptions. Our results are algorithm agnostic for a range of loss functions, and only depend linearly on the averaged learning error across the trajectory.
arXiv Detail & Related papers (2022-04-25T13:26:11Z)
RuleBert: Teaching Soft Rules to Pre-trained Language Models [21.69870624809201]
We introduce a classification task where, given facts and soft rules, the PLM should return a prediction with a probability for a given hypothesis. We propose a revised loss function that enables the PLM to learn how to predict precise probabilities for the task. Our evaluation results show that the resulting fine-tuned models achieve very high performance, even on logical rules that were unseen at training.
arXiv Detail & Related papers (2021-09-24T16:19:25Z)
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation [116.48885692054724]
We propose a reinforcement learning based selective pseudo-labeling method for semi-supervised domain adaptation. We develop a deep Q-learning model to select both accurate and representative pseudo-labeled instances. Our proposed method is evaluated on several benchmark datasets for SSDA, and demonstrates superior performance to all the comparison methods.
arXiv Detail & Related papers (2020-12-07T03:37:38Z)

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