8580 papers
This paper introduces Margin and Consistency Supervision (MaCS), an architecture-agnostic regularization framework that combines a hinge-squared margin penalty and a consistency regularizer to simultaneously enhance the calibration, robustness, and generalization of deep vision models without requiring additional data or architectural changes.
The paper introduces SEA-PEFT, a self-auditing parameter-efficient fine-tuning framework that automates adapter configuration through a dynamic search-audit-allocate loop, achieving significant performance improvements in few-shot 3D medical image segmentation without requiring manual design or extensive computational resources.
This paper empirically evaluates the effectiveness and limitations of many-shot prompting for test-time adaptation in large language models, finding that while it benefits structured tasks with high information gain, its performance is highly sensitive to selection strategies and often yields limited improvements for open-ended generation.
ReflexiCoder is a novel reinforcement learning framework that internalizes structured self-reflection and self-correction capabilities into an LLM's weights, enabling it to autonomously generate, debug, and optimize code without external feedback while achieving state-of-the-art performance and improved token efficiency across multiple benchmarks.
The paper introduces STEP, a continuous-time stochastic framework that models temporal link prediction as a sequential forecasting problem via Poisson-driven motif transitions, achieving significant precision gains and lower runtime compared to state-of-the-art baselines while remaining compatible with existing graph neural networks.
This paper proposes ROSE, a reordered SparseGPT method that enhances one-shot LLM pruning accuracy by adaptively reordering weights based on estimated column and block pruning losses to address the suboptimal performance caused by predefined left-to-right pruning orders in layers with columnar patterns.
PixARMesh is a novel autoregressive method that directly reconstructs complete, high-fidelity, and artist-ready 3D indoor scene meshes from a single RGB image by jointly predicting object layout and geometry within a unified model, eliminating the need for implicit fields or post-hoc optimization.
This paper proposes three bias mitigation techniques—top-k concept filtering, removal of biased concepts, and adversarial debiasing—to address information leakage in Concept Bottleneck Models, thereby achieving superior fairness-performance tradeoffs for interpretable image classification compared to prior work.
This paper introduces Reference-guided Policy Optimization (RePO), a novel framework that combines reinforcement learning with verifiable rewards and supervised reference guidance to effectively balance exploration and exploitation in molecular optimization tasks where only single-reference data is available, thereby outperforming existing SFT and RLVR baselines.
This paper proposes an integrated framework combining a node transformer architecture with BERT-based sentiment analysis to model stock market graphs and social media sentiment, demonstrating superior forecasting accuracy (0.80% MAPE) and directional precision compared to traditional ARIMA and LSTM models across 20 S&P 500 stocks from 1982 to 2025.
This paper proposes "Artificial Replay," a new experimental design that reuses recorded rewards from a single policy trajectory to enable unbiased, cost-effective, and low-variance comparisons between multi-armed bandit algorithms, thereby significantly reducing the number of required user interactions compared to traditional independent restarts.
This paper introduces Weak-SIGReg, a computationally efficient covariance regularization method derived from Sketched Isotropic Gaussian Regularization that stabilizes deep learning optimization and prevents representation collapse in low-bias architectures like Vision Transformers without relying on standard architectural priors.
This paper demonstrates that addressing the ecological fallacy by modeling an author's language context through a specific task called HuLM, particularly during fine-tuning (HuFT) or continued pre-training, significantly improves the performance of an 8B Llama model across multiple downstream tasks compared to standard training methods.
This paper presents an FPGA accelerator that eliminates the memory-bound bottleneck of Gated DeltaNet decode by persistently storing the full recurrent state in on-chip BRAM, achieving 4.5 faster inference and up to 60 higher energy efficiency compared to an NVIDIA H100 GPU.
This paper proposes a Structured Style-Rewrite Framework that combines explicit disentanglement of stylistic dimensions with implicit Chain-of-Thought conditioning to enable small language models to achieve high-fidelity, consistent character role-playing without requiring explicit reasoning tokens during inference.
This paper proposes an interpretable modeling approach that integrates person-level psychological traits with situational context features derived from social media data to predict dynamic mental well-being, demonstrating that theory-driven methods offer competitive performance and greater human-understandable insights compared to standard language model embeddings.
The paper proposes Omni-Masked Gradient Descent (OMGD), a memory-efficient optimization method that achieves a strictly improved nonconvex convergence rate of and demonstrates consistent empirical improvements in large language model training tasks.
This paper introduces TADPO, a novel reinforcement learning framework that extends Proximal Policy Optimization with off-policy teacher guidance and on-policy student exploration to enable zero-shot sim-to-real, high-speed autonomous driving on full-scale off-road vehicles navigating complex, unmapped terrain.
The paper introduces EvoESAP, an evolutionary framework that optimizes non-uniform layer-wise sparsity allocations for Sparse Mixture-of-Experts models using a stable, speculative-decoding-inspired metric called ESAP, significantly improving open-ended generation performance while maintaining competitive accuracy compared to traditional uniform pruning methods.
This paper identifies that learning stagnation in PPO arises from poor sample-based loss estimates due to excessive step sizes relative to gradient noise, proposing that scaling to over one million parallel environments effectively mitigates this issue and enables monotonic performance improvements up to one trillion transitions.