5693 papers
LongAudio-RAG is a hybrid edge-cloud framework that enables precise, low-hallucination question answering over multi-hour audio streams by converting recordings into timestamped event records for SQL-based retrieval, which then grounds Large Language Model responses in structured evidence rather than raw audio.
This paper introduces Direct Kolen-Pollack Predictive Coding (DKP-PC), a novel algorithm that enhances the efficiency and scalability of biologically inspired predictive coding by establishing direct learnable feedback connections from the output to all hidden layers, thereby reducing error propagation time complexity from O(L) to O(1) while mitigating vanishing updates and maintaining local learning.
This paper demonstrates that strategically screening and training on a carefully selected subset of high-quality, relevant data sources is sufficient to achieve statistically optimal shared feature extraction, even when discarding a substantial portion of available data.
The paper introduces Emotion Collider (EC-Net), a hyperbolic hypergraph framework that leverages Poincaré-ball embeddings, bidirectional message passing, and contrastive learning to achieve robust and noise-resilient multimodal sentiment analysis by preserving high-order semantic relations and enhancing class separation.
ModalImmune is a training framework that enhances the robustness of multimodal systems against input channel loss by intentionally collapsing selected modality information during training through a combination of adaptive regularization, targeted intervention, and certified meta-parameter adaptation.
This paper introduces FlyGM, a whole-brain connectomic graph model that leverages the exact static neural architecture of an adult fruit fly to achieve stable, sample-efficient whole-body locomotion control in embodied reinforcement learning without task-specific tuning.
This paper introduces a framework for operational certification of split conformal predictors that moves beyond marginal coverage by providing finite-sample guarantees for critical deployment metrics like commitment frequency and error exposure through Small-Sample Beta Correction, an independent audit-based auditing protocol, and a geometric analysis of Pareto trade-offs.
This paper demonstrates that neural networks learning equivariant operators in a latent space can effectively generalize to out-of-distribution symmetric transformations on simple datasets like rotated MNIST, while also highlighting the significant challenges involved in scaling this approach to more complex data.
This paper establishes a comprehensive multi-KPI benchmark for Multi-Agent Reinforcement Learning in urban energy management using the CityLearn environment, demonstrating that Decentralized Training with Decentralized Execution (DTDE) consistently outperforms Centralized Training with Decentralized Execution (CTDE) in both average and worst-case performance while offering greater resilience and sustainability.
This paper introduces RAmmStein, a deep reinforcement learning framework that optimizes liquidity provision in concentrated Automated Market Makers by solving an impulse control problem via a Hamilton-Jacobi-Bellman quasi-variational inequality, thereby significantly reducing rebalancing frequency and gas costs while maximizing net returns through regime-aware, mean-reversion-informed decision-making.
This paper benchmarks four GNN architectures on molecular regression tasks, demonstrating that a hierarchical fusion framework combining GNNs with molecular fingerprints outperforms standalone models by over 7% in RMSE, while CKA analysis reveals that GNN and fingerprint embeddings occupy highly independent latent spaces despite high convergence among isotopic GNN architectures.
The paper introduces MrBERT, a family of efficient, open-source multilingual encoders built on the ModernBERT architecture that achieves state-of-the-art performance in specific languages and specialized domains while leveraging Matryoshka Representation Learning to reduce inference and storage costs.
The paper introduces AVDE, a lightweight and efficient autoregressive framework that leverages contrastive learning and multi-scale token prediction to decode EEG signals into coherent images, outperforming state-of-the-art methods with significantly fewer parameters while mimicking the hierarchical nature of human visual perception.
CeRA overcomes the linear performance ceiling of Low-Rank Adaptation (LoRA) in complex reasoning tasks by introducing a weight-level parallel adapter with SiLU gating and structural dropout to induce manifold expansion, thereby achieving superior spectral efficiency and preventing rank collapse.
This paper addresses the scarcity of expert textual relevance labels in large-scale app store search by leveraging a specialized, fine-tuned LLM to generate millions of high-quality labels, which, when used to augment the production ranker, significantly improves both offline metrics and real-world conversion rates, particularly for tail queries lacking reliable behavioral data.
This paper introduces the first end-to-end differentiable optical particle detector simulator that unifies simulation, calibration, and reconstruction into a single gradient-based framework, demonstrating improved accuracy, speed, and flexibility for analyzing large-scale neutrino detectors compared to traditional methods.
This paper introduces Attn-QAT, the first systematic 4-bit quantization-aware training framework for attention mechanisms that ensures stable FP4 training and inference by matching low-precision recomputation in the backward pass and correcting implicit precision assumptions, thereby eliminating quality drops and delivering up to 1.5x speedup on FP4-capable GPUs without relying on outlier-mitigation heuristics.
This paper introduces a new class of non-equal volume partitions that achieve a lower expected star discrepancy and improved upper bounds compared to classical jittered sampling, thereby providing a theoretical foundation for their use in high-dimensional numerical integration.
This paper introduces a reproducible, scalable framework for evaluating multimodal models on ECG signals by decomposing reasoning into "Perception" (verified via code generation) and "Deduction" (verified via retrieval against clinical criteria) to address the limitations of existing manual or superficial evaluation methods.
This paper proposes a tractable two-layer framework combining a Hidden Markov Model for inferring rival energy states and a Deep Q-Network for decision-making to optimize 2026 Formula 1 energy strategies under partial observability, specifically addressing the "counter-harvest trap" where opponents deliberately mask their deployment signals.