arxiv arXiv cs.LG · 1d ago

Robust Diffusion Models via Divergence-Induced Weighted Denoising

A new training method replaces MSE loss in diffusion models with an f-divergence-based transformation, creating a robust surrogate that improves performance under data contamination. The approach uses local divergence constructions under DDPM's Gaussian reverse-kernel, reducing the training objective to a one-dimensional function of denoising error, with bounded-influence divergences suppressing large errors and enhancing stability.

arxiv arXiv cs.LG · 1d ago

Introducing Quantum Measurement Temperature to Stabilize Hybrid QNN Training

A learnable scaling parameter called Quantum Measurement Temperature (QMT) is introduced to rescale quantum measurement outputs in hybrid quantum neural networks. This approach mitigates measurement-induced logit contraction, enhancing gradient magnitude and stability during training without altering the quantum circuit or measurement operators. Experiments show improved logit separation, gradient strength, and classification accuracy in protein and image classification tasks.

arxiv arXiv cs.LG · 1d ago

Stationary Robust Mean-Field Games under Model Mismatches

This paper introduces a stationary mean-field game framework that directly incorporates distributional model uncertainty into population-coupled dynamics. It establishes a robust dynamic programming principle, proves existence of a stationary robust equilibrium, and presents the first algorithm with convergence guarantees. The mean-field solution approximates finite-population equilibria and provides explicit non-asymptotic error bounds under model uncertainty.

arxiv arXiv cs.LG · 1d ago

Training-Free Task Classification for Multi-Task Model Merging

SiM enables dynamic routing in multi-task model merging without additional training or task ID access. It uses SVD-based manifold approximations and projects test inputs onto precomputed task manifolds to route inputs to relevant experts, improving performance and reducing the gap to individual expert levels.

arxiv arXiv cs.LG · 1d ago

Importance-Weighted On-Policy Distillation Addresses Position Bias

On-Policy Distillation (OPD) suffers from position bias where later tokens provide poor supervision. We introduce Importance-Weighted On-Policy Distillation (IW-OPD), which assigns weights based on distribution discrepancy, prioritizing early tokens. IW-OPD converges faster and achieves up to 6.9 point performance gains on AIME-2025.

arxiv arXiv cs.LG · 1d ago

Scalable Bayesian Models for Stellar Flare Detection

A generative surrogate framework using a Variational Autoencoder approximates Gaussian Process priors, bypassing costly covariance operations. The VAE+Hidden Markov Model architecture enables fast, scalable stellar flare detection in large astronomical time series, matching exact models in structural fidelity while reducing computational time significantly.

arxiv arXiv cs.AI · 1d ago

Select-to-Act: Hierarchical RL with Adaptive Language Guidance

HRLLI introduces a hierarchical reinforcement learning framework that adapts natural-language instructions dynamically during decision-making. It decomposes instructions into stage-specific guidance elements and uses a select-to-act paradigm to enable real-time selection of relevant instruction pieces, improving sample efficiency and performance in complex environments.

arxiv arXiv cs.AI · 1d ago

Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning

Parsimoniously activated dictionary learning (PADL) establishes a structured generative model with auxiliary latent variables, enabling maximum a posteriori estimation. This framework provides generalization guarantees and an analytical characterization of the tradeoff between sparsity, storage cost, and reconstruction accuracy, allowing data-driven hyperparameter estimation. The resulting algorithm achieves better reconstruction performance and accelerates inference in vision-language models.

arxiv arXiv cs.AI · 1d ago

HyperAdapter: Structured Hyperedge Adaptation for Vision Transformer Fine-Tuning

HyperAdapter introduces a hypergraph-based adapter that performs structured, group-aware adaptation in vision transformers by operating in hyperedge space rather than token space. It uses prototype-based assignments to build a soft hypergraph, aggregates token features into hyperedge representations, applies lightweight adaptation, and diffuses updates back via hypergraph structure, enabling explicit structural inductive bias while maintaining efficiency. Experiments show consistent performance gains over baseline PEFT methods, especially on tasks requiring structured reasoning.

arxiv arXiv cs.AI · 1d ago

P4IR Framework Improves LLM-Based Code Compliance Accuracy

P4IR, a two-stage framework, uses supervised fine-tuning and Group Relative Policy Optimization to enhance large language model-based automated code compliance systems. It reduces tree edit and token-level Levenshtein distances by up to 23.8% and 38.6% respectively, outperforming leading LLMs like Claude Opus, GPT-5.2, and GLM-4.7 in zero-shot settings with few-shot prompting, and reduces false positives by a small but statistically significant margin.

arxiv arXiv cs.AI · 1d ago

SciVerseGym: Reinforcement Learning Environment for Crystal Discovery

SciVerseGym introduces a Gymnasium-compatible environment that frames crystal discovery as a Markov decision process. It enables agents to perform chemically meaningful edits on atomic structures and receive feedback from configurable evaluators, supporting diverse actions and observation types with machine-learned potentials or ASE-compatible calculators.

arxiv arXiv cs.AI · 1d ago

Imagine to Ensure Safety in Hierarchical Reinforcement Learning

The method combines a learnable world model with high- and low-level policies to enable safe exploration in long-horizon tasks. The high-level policy guides exploration toward safe subgoals, while the low-level policy uses imagined rollouts to prevent unsafe behaviors, outperforming existing Safe RL methods in success rate and constraint satisfaction across diverse tasks.

arxiv arXiv cs.AI · 1d ago

Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation

Fed-CausalDiff introduces a federated causal diffusion framework that enables do-simulation in decentralized settings. It decomposes latent state evolution into global and local components, allowing decoupled synchronisation to reduce communication cost while maintaining accurate policy evaluation and ATE estimation.

arxiv arXiv cs.AI · 1d ago

Importance-Weighted On-Policy Distillation Addresses Position Bias

On-Policy Distillation (OPD) suffers from position bias where later tokens provide poor supervision. Importance-Weighted OPD (IW-OPD) assigns dynamic weights based on distribution discrepancy, prioritizing early tokens and suppressing late ones. IW-OPD converges faster and achieves up to 6.9 point performance gains on AIME-2025 compared to standard OPD.

arxiv arXiv cs.LG · 1d ago

Reward-free Pretraining for Reinforcement Learning via Occupancy Coverage Maximization

ROVER enables reward-free pretraining by maximizing occupancy coverage in state space, using a learned world model to estimate occupancy without density or entropy estimation. It introduces a virtual sink state to balance exploration of known and unknown regions, achieving more uniform coverage and better downstream performance in tabular and pixel-based navigation tasks.

arxiv arXiv cs.LG · 1d ago

Central limit theorem for averaged Adam optimizer

The article establishes a central limit theorem for the averaged Adam optimizer, showing convergence at order n^{-1/2}. This rate matches classical stochastic approximation algorithms, with the covariance expressed in terms of the algorithm's properties at the attractor state.

arxiv arXiv cs.LG · 1d ago

BIPC Framework Accelerates Mixed-Integer Optimization with Machine Learning

The BIPC framework reduces solution time for large-scale mixed-integer programs by identifying a backdoor subset of variables that drive computational complexity. Using supervised learning, it predicts backdoor variable values and intervals, then solves a reduced problem with these predictions, achieving significant speedups with minimal quality loss. This enables rapid, high-quality solutions under parameter perturbations in real-world systems like power and supply chains.

arxiv arXiv cs.LG · 1d ago

Deep Learning with O(log N) Parallel Time Complexity

Hierarchical Block-Local Learning (HBLL) enables deep neural network training in O(log N) parallel time complexity, eliminating the need for full backpropagation. HBLL decomposes networks into hierarchically linked blocks and achieves competitive performance on vision and language tasks, with extensions to recurrent architectures.

arxiv arXiv cs.LG · 1d ago

Analytic Policy Gradients for Efficient Continuous Control

Analytic Policy Gradients (APG) enables exact gradient computation via backpropagation through simulation when environment dynamics are differentiable. APG outperforms Proximal Policy Optimization (PPO) on four continuous control tasks, showing superior sample and learning efficiency with a segmented backpropagation scheme that reduces gradient degradation on long-horizon tasks.

arxiv arXiv cs.LG · 2d ago

Muon Optimizer: Power, Limits, and a River-Valley Theory

A new trajectory-level theory reveals Muon accelerates early in optimization along the information-bearing river direction but converges slowly near the bottom, unlike gradient descent. With momentum, Muon's orthogonalized updates remove residual scale information, leading to overshooting and oscillation. The study advocates a two-stage approach—using Muon early and switching to gradient descent-like optimizers later—for improved LLM training performance.