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Newest papers, page 233
Steering the Fidelity in State Preparation on Noisy Bosonic Systems with Risk-Seeking Optimization
In order to execute quantum algorithms on bosonic quantum computers, it is necessary to encode their inputs into a desired target quantum state, with a process called state preparation.How Low Can You Go: Fine-Tuning LLMs with Bayesian Optimization (LlMBO)
Recently, Morris et al. introduced TinyLoRA, a strategy for LLM fine-tuning which introduces arbitrarily few parameters, and demonstrated accuracy improvements using less than $10$ parameters rather…Towards Safe Autonomy for Computer Use
The safety of LLM-based agents currently rests on alignment training and safety classifiers, neither of which can rule out that a sophisticated indirect prompt injection (IPI) goes through and…Approximating Optimal Policies in Heterogeneous Asynchronous Multi-player Bandits
Motivated by applications to cognitive radio networks, multi-player multi-armed bandits (MMAB) have attracted considerable attention in recent years.Two For The Price Of One: Curvature-Aware Pruning Preserves Influence Functions Too
Post-training pruning enables efficient deployment, but conventional methods do not explicitly preserve internal-gradient alignment between sparse and dense models.Euclidean Distance-Based Flow Matching
Flow Matching has recently become popular as a technique for generative modeling with applications in many domains.Beyond the Encoder: Object-Centric Representations Emerging from Segmentation Foundation Models
Foundation models are increasingly used as feature extractors for downstream tasks with limited data, typically through representations obtained from their encoders.Understanding Circulant Permutation to Extend the CMinHash Estimator
CMinHash promises a new direction for designing MinHash algorithms with reduced variance and storage space via circulant permutations.Unbalanced W-Flow: Unbalanced Wasserstein Drifting under Finite-Batch Marginal Fluctuations
Teacher-free one-step generative models such as W-Flow learn directly from distributional objectives, with the Sinkhorn divergence providing the strongest empirical performance among the energies…Last-Iterate Convergence of Clipped SGD with Linear Decay
In this paper, we study the last-iterate convergence of clipped stochastic gradient descent under heavy-tailed noise.Linear Last-Iterate Convergence Rates for Optimistic Learning in Harmonic Games
We prove that the last iterate of Optimistic Follow-The-Regularized-Leader (OFTRL) converges at a linear rate to a Nash equilibrium in multiplayer harmonic games.LLM Agents for Automated Post-Training via Structured Recipe Optimization
Automated post-training transforms pre-trained large language models (LLMs) into models capable of handling diverse domains by automatically constructing and optimizing a recipe of coupled decisions…When More Objectives Make Identification Easier: The Role of Preferences and Feedback
Best-arm identification (BAI) aims to identify the optimal arm using as few samples as possible.GeoMER: A Joint Euclidean-Hyperbolic Geometry Framework for Multimodal Emotion Recognition
Multimodal Emotion Recognition (MER) aims to understand human emotions by integrating information from multiple modalities such as speech and text.Fairness under Prediction Error in Predict-then-Optimize Pipelines
Fairness constraints in predict-then-optimize pipelines are necessarily enforced on predicted outcomes $\hat{y}$, since true outcomes $y$ are unobserved at decision time.Autonomous Cyber Operations Gym for Training and Evaluating AI Models for Defence
This paper proposes an Autonomous Cyber Operations (ACO) gym, a unified framework integrating high-fidelity network emulation, realistic user behaviour modelling for benign network traffic and…ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models
Reinforcement learning (RL) post-training improves robot foundation models (RFMs) beyond their demonstrations, but real-world deployment demands more: learning new tasks efficiently while retaining…PhysicsSliders: Learning Physical Controls for Audio Foley Generation
Physical properties provide intuitive controls for sounds made by interacting objects.Does self-distillation improve reasoning?
Self-distillation is an emerging approach that trains a model to imitate its own predictions when conditioned on privileged information, such as correct answers or environmental feedback.Can an Agent Learn to Predict Its Own Failure? Outcome-Grounded Confidence Calibration in LLM Agents
Controlling an acting LLM agent means deciding, at each step of a long-horizon task, whether to trust its next action, abstain, or hand off to a human.