Gaussian Process Bandit Optimization with Machine Learning Predictions and Application to Hypothesis Generation

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Many real-world optimization problems involve an expensive ground-truth oracle (e.g., human evaluation, physical experiments) and a cheap, low-fidelity prediction oracle (e.g., machine learning models, simulations). Meanwhile, abundant offline data (e.g., past experiments and predictions) are often available and can be used to pretrain powerful predictive models, as well as to provide an informative prior. We propose Prediction-Augmented Gaussian Process Upper Confidence Bound (PA-GP-UCB), a novel Bayesian optimization algorithm that leverages both oracles and offline data to achieve provable […]

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Heterogeneous Agent Collaborative Reinforcement Learning

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We introduce Heterogeneous Agent Collaborative Reinforcement Learning (HACRL), a new learning paradigm that addresses the inefficiencies of isolated on-policy optimization. HACRL enables collaborative optimization with independent execution: heterogeneous agents share verified rollouts during training to mutually improve, while operating independently at inference time. Unlike LLM-based multi-agent reinforcement learning (MARL), HACRL does not require coordinated deployment, and unlike on-/off-policy distillation, it enables bidirectional mutual learning among heterogeneous agents rather than one-directional teacher-to-student transfer. Building on this paradigm, we propose […]

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Graph-Dictionary Signal Model for Sparse Representations of Multivariate Data

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arXiv:2411.05729v2 Announce Type: replace-cross Abstract: Representing and exploiting multivariate signals requires capturing relations between variables, which we can represent by graphs. Graph dictionaries allow to describe complex relational information as a sparse sum of simpler structures, but no prior model exists to infer such underlying structure elements from data. We define a novel Graph-Dictionary signal model, where a finite set of graphs characterizes relationships in data distribution as filters on the weighted sum of their Laplacians. We propose […]

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Rich Insights from Cheap Signals: Efficient Evaluations via Tensor Factorization

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arXiv:2603.02029v2 Announce Type: replace-cross Abstract: Moving beyond evaluations that collapse performance across heterogeneous prompts toward fine-grained evaluation at the prompt level, or within relatively homogeneous subsets, is necessary to diagnose generative models’ strengths and weaknesses. Such fine-grained evaluations, however, suffer from a data bottleneck: human gold-standard labels are too costly at this scale, while automated ratings are often misaligned with human judgment. To resolve this challenge, we propose a novel statistical model based on tensor factorization that merges […]

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LLMs for Game Theory: Entropy-Guided In-Context Learning and Adaptive CoT Reasoning

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We propose a novel LLM-based framework for reasoning in discrete, game-theoretic tasks, illustrated with emph{Tic-Tac-Toe}. The method integrates in-context learning with entropy-guided chain-of-thought (CoT) reasoning and adaptive context retrieval. The model dynamically adjusts both the number of retrieved examples and reasoning paths according to token-level uncertainty: concise reasoning with minimal context is used when uncertainty is low, whereas higher uncertainty triggers expanded multi-path CoT exploration. Experimental evaluation against a sub-optimal algorithmic opponent shows that entropy-aware adaptive reasoning substantially […]

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Fine-tuning vision-language models on memory-constrained devices

Fine-tuning vision-language models on memory-constrained devices

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Fine-tuning vision-language models on memory-constrained devices A new hybrid optimization approach allows edge devices to fine-tune vision-language models using only forward passes, achieving up to 7% higher accuracy than existing techniques. Machine learning Rupak Vignesh Swaminathan Jing Liu Nathan Susanj January 08, 11:41 AM January 09, 12:27 PM Fine-tuned vision-language models (VLMs) have shown remarkable performance across many computer vision tasks. However, backpropagation the standard method for adjusting model weights during fine tuning, which works backward from output […]

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MEXC 2025 Report: Zero-Fee Strategy Delivers $1.1B in User Savings, Capturing Leading Market Share

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Victoria, Seychelles, January 29, 2026 MEXC, the fastest-growing global cryptocurrency exchange, redefining a user-first approach to digital assets through true Zero-Fee trading, today released its 2025 Zero-Fee Strategy Annual Report. The ongoing commitment not only saved users a total of 1.1 billion USDT in fees but also bolsters both mainstream growth and emerging asset visibility, driving balanced development across the entire crypto landscape. The platform’s removal of fees across 3,026 spot trading pairs and 203 futures pairs resulted […]

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Pierre Teilhard de Chardin: The New Spirit

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Humanity awakens to Time’s convergent architecture: a cone ascending toward divine unity. Evolution provides Christianity its perfect cosmological framework—Christ occupies the apex, charity becomes the force drawing reality upward. Material and spiritual progress merge. Love of God, neighbor, and earthly advancement fuse into one indivisible act of cosmic synthesis.

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