arxiv arXiv cs.LG · 17h ago

Escaping the Variance Trap: Jacobian-Free Dynamics for Root-Finding Bilevel Optimization

The authors identify a critical flaw termed the Variance Trap, which arises when stochastic root-finding problems are forced into minimization frameworks via squared residuals. Standard bilevel minimization algorithms require estimating hypergradients involving implicit Jacobians that act as noise amplifiers in stochastic settings. To address this, the paper formalizes Root-Finding Bilevel Optimization (RF-BO) as a distinct problem class to bypass this pathology. A Jacobian-free solution using Two-Time-Scale Stochastic Approximation (TTSA) is proposed to update directly along the root error. The study provides the first non-asymptotic convergence guarantees for TTSA in this setting under Markovian noise. Experiments show a 2.6% top-1 accuracy gain in SimCLR and 17x faster convergence in non-linear ODE control compared to baselines. Additionally, the framework achieves significantly improved entropy stability in reinforcement learning and an 11.1% quality improvement in generative modeling.

arxiv arXiv cs.LG · 17h ago

RQ-TTSA: Distribution-Aware Robust Bilevel Optimization with Quantile-Guided Huber Updates

The authors propose RQ-TTSA, a distribution-aware framework designed to address instability in bilevel optimization caused by heavy-tailed stochastic noise. Unlike existing variance-reduction techniques that rely on myopic magnitude checks, this method uses historical gradient buffers to estimate rolling quantiles for adaptive Huber-style clipping. This approach preserves local optimization geometry while strictly bounding effective variance under nonconvex-strongly convex assumptions with infinite-variance noise. Theoretical analysis derives a convergence rate of O(T^(-(p-1)/(3p-2))) that recovers optimal dependence on the heavy-tailed parameter p. Empirical evaluations across six diverse tasks, including vision benchmarks and offline reinforcement learning, show consistent outperformance over state-of-the-art baselines. RQ-TTSA eliminates divergence spikes and ensures stable convergence with negligible computational overhead of approximately 2.7 percent.

media r/LocalLLaMA · 18h ago

Colony: An Educational Simulation of LLM Attention Mechanisms Using Agent-Based Analogies

Colony is an educational resource designed to explain the attention mechanism of Large Language Models through simple analogies involving agents. The simulation places these agents within a board environment inspired by Conway's Game of Life. Each agent in the system represents a specific role within the self-attention block mechanism of an LLM. This visual approach allows users to observe how information flows and interacts during the attention process. The project is available as an open-source tool for those interested in exploring these concepts without complex mathematics. It serves as a fun and accessible way to understand the internal workings of transformer models.

arxiv arXiv cs.LG · 21h ago

A Differentiable Atari VCS for Explainable AI

A fully differentiable emulator of the Atari 2600 VCS is presented, reproducing all 64 ALE games with bit-for-bit accuracy in RAM and screen output. The system enables gradient-based explainable AI by providing a complex, fully known ground truth, with both Julia and JAX implementations validated against a reference emulator and supporting high-throughput training on GPUs.

arxiv arXiv cs.LG · 21h ago

AdaR: Adaptive Recurrent Message Passing for Graph Test-Time Computing

AdaR enables flexible test-time computing on graphs without parameter changes by using adaptive recurrence. It derives step dependence as a necessary and sufficient condition for convergence and incorporates normalized step information and representation-target relations into recurrent updates, guided by gradient-based supervision signals. Empirical results show AdaR outperforms strong baselines in both inductive and transductive graph learning settings.

arxiv arXiv cs.LG · 21h ago

Speech-Text Models Latently Transcribe Speech in Intermediate Layers

Interleaved speech-language models undergo an implicit transcription phase where spoken words become decodable as text tokens in intermediate layers, despite no speech recognition training. Up to 77% of the data shows the spoken word appearing as a top candidate text prediction, followed by a transition to text-based next-word prediction before returning to speech. This behavior is influenced by interleaved training and text LM initialization, and correlates with spoken knowledge performance.

arxiv arXiv cs.LG · 21h ago

Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation

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

arxiv arXiv cs.LG · 21h ago

Prompt-Side Preprocessing Enhances Edge AI Accuracy

A structured prompt framework improves local LLM accuracy in environmental monitoring by transforming raw sensor data into enriched textual representations. Evaluations on indoor and outdoor datasets show local model accuracy increases from 50.9% to 81.7% indoors and from 63.7% to 89.3% outdoors with enriched prompts, while maintaining low latency near 0.22 seconds in no-chain-of-thought mode.

arxiv arXiv cs.LG · 22h ago

The Scissors Effect: Resize Diversity Hurts Robust Surrogate Transfer

Input diversity, a common practice in transfer attacks, improves success on standard surrogates but reduces it on robust ones. This regime-dependent effect, called the Scissors Effect, is driven by gradient geometry, with resize operations degrading alignment in robust models. A training-free rule (CG-DI) adjusts diversity based on local gradient consistency to preserve attack success across surrogate types.

arxiv arXiv cs.LG · 22h ago

Generative Robust Optimisation Framework

Generative Robust Optimisation (GRO) introduces a deep generative model to define uncertainty sets, capturing nonlinear correlations, asymmetry, and multimodality. A five-point evaluation framework assesses neural network-based uncertainty sets across reconstruction fidelity, distribution matching, latent regularity, robust relevance, and computational tractability, with experiments validating GRO's effectiveness in production planning and facility location problems.

arxiv arXiv cs.LG · 22h 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 · 22h ago

Deep material network for homogenization of piezoelectric composites

A piezoelectric deep material network (PDMN) is proposed to efficiently homogenize two-phase piezoelectric composites. The framework embeds electromechanical homogenization relations into its architecture, enabling physics-informed, semi-analytical predictions with over three orders of magnitude lower computational cost than direct numerical simulation, validated on PVDF-LiNbO3 and viscoelastic-piezoelectric composites under nonlinear loading.

arxiv arXiv cs.LG · 22h ago

Concept-Constrained Prompt Learning for Few-Shot CLIP Adaptation

CCPL introduces a lightweight framework that anchors class prompts to frozen concept prototypes, improving few-shot CLIP adaptation. It achieves better base-to-new performance on DTD and EuroSAT compared to CoOp, with consistent gains from text-space concept regularization, though results vary by dataset and protocol.

arxiv arXiv cs.LG · 22h 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 · 22h 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 · 22h 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 · 22h 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 · 23h ago

Geometry-Aware Online Scheduling for LLM Serving

A new scheduling algorithm, Smallest Volume First (SVF), reduces LLM inference latency by optimizing key-value cache management. Theoretical analysis shows a worst-case competitive ratio reduced from 48 to 5, with 1-bit SVF achieving strong performance using minimal information. Evaluations on Llama-3.1 models confirm improvements in both average and tail latency, with the approach integrated into vLLM.

arxiv arXiv cs.AI · 23h ago

Hypothesis-Driven Skill Optimization for LLM Agents

HDSO enables safe, auditable skill updates for LLM agents without training, using falsifiable hypotheses and validation. On ALFWorld, it improves Qwen3-8B by +6.9 Avg. SR points and maintains a +7.1-point gain under noisy feedback, with validated skills transferable across runs and models when diagnostic alignment is achieved.

arxiv arXiv cs.AI · 23h ago

Flow Annealing Posterior Sampling for Function-Space Regression and Inverse Problems

FAPS is the first function-space posterior sampling framework that unifies stochastic-process regression and PDE inverse problems. It uses pretrained flow-matching priors and Langevin correction with low-rank covariance preconditioning to enable efficient, accurate posterior inference from sparse, noisy data with coherent uncertainty quantification.