technocracy

arXiv:2603.00695v1 Announce Type: new Abstract: Multi-modal object Re-Identification (ReID) aims to exploit complementary information from different modalities to retrieve specific objects. However, existing methods often rely on hard token filtering or simple fusion strategies, which can lead to the loss of discriminative cues and increased background interference. To address these challenges, we propose STMI, a novel multi-modal learning framework consisting of three key components: (1) Segmentation-Guided Feature Modulation (SFM) module leverages SAM-generated masks to enhance foreground representations and […]

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Artificial Intelligence has its own way of impressing us with its potential and wonders. It is an incredibly growing field and will definitely have an immense effect on our lives. In my opinion, automating manual labor and work sounds like an amazing idea; people wouldn’t need to bother about whether there is going to be traffic in their route to work, or you wouldn’t have to review through spelling/grammar errors in your lengthly research paper for English. AI […]

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Utilizing feature stores like Feast and distributed compute frameworks like Ray in production machine learning systems The post Scaling Feature Engineering Pipelines with Feast and Ray appeared first on Towards Data Science.

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arXiv:2601.05373v1 Announce Type: new Abstract: Early diagnosis of breast cancer is crucial for improving survival rates. Radiomics and deep learning (DL) have shown significant potential in assisting radiologists with early cancer detection. This paper aims to critically assess the performance of radiomics, DL, and ensemble techniques in detecting cancer from screening mammograms. Two independent datasets were used: the RSNA 2023 Breast Cancer Detection Challenge (11,913 patients) and a Mexican cohort from the TecSalud dataset (19,400 patients). The ConvNeXtV1-small […]

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Learning from a stream of tasks usually pits plasticity against stability: acquiring new knowledge often causes catastrophic forgetting of past information. Most methods address this by summing competing loss terms, creating gradient conflicts that are managed with complex and often inefficient strategies such as external memory replay or parameter regularization. We propose a reformulation of the continual learning objective using Douglas-Rachford Splitting (DRS). This reframes the learning process not as a direct trade-off, but as a negotiation between […]

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arXiv:2602.04903v1 Announce Type: new Abstract: Feature steering has emerged as a promising approach for controlling LLM behavior through direct manipulation of internal representations, offering advantages over prompt engineering. However, its practical effectiveness in real-world applications remains poorly understood, particularly regarding potential trade-offs with output quality. We show that feature steering methods substantially degrade model performance even when successfully controlling target behaviors, a critical trade-off. Specifically, we evaluate Goodfire’s Auto Steer against prompt engineering baselines across 14 steering queries […]

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arXiv:2601.16446v1 Announce Type: new Abstract: Deep learning models are effective for sequential data modeling, yet commonly used activation functions such as ReLU, LeakyReLU, and PReLU often exhibit gradient instability when applied to noisy, non-stationary financial time series. This study introduces BrownianReLU, a stochastic activation function induced by Brownian motion that enhances gradient propagation and learning stability in Long Short-Term Memory (LSTM) networks. Using Monte Carlo simulation, BrownianReLU provides a smooth, adaptive response for negative inputs, mitigating the dying […]

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arXiv:2601.14456v1 Announce Type: new Abstract: Recent work shows that fine-tuned Large Language Models (LLMs) can achieve high valid plan rates on PDDL planning tasks. However, it remains unclear whether this reflects transferable planning competence or domain-specific memorization. In this work, we fine-tune a 1.7B-parameter LLM on 40,000 domain-problem-plan tuples from 10 IPC 2023 domains, and evaluate both in-domain and cross-domain generalization. While the model reaches 82.9% valid plan rate in in-domain conditions, it achieves 0% on two unseen […]

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Automated Speciation (Bifurcation) When the Regulator returns UNSAT (identifying that performance and diversity constraints are mutually exclusive), the system triggers a Bifurcation Event. This partitions the population into specialized sub-cradles, proved by Lean 4 to be Pareto-optimal transitions. JAX-Native Parallelism Implementation utilizes JAX collective operations for O(1) scaling across multi-GPU/TPU nodes. The Symbolic Tier (Z3/Lean) runs asynchronously on CPU nodes, maintaining high-throughput JaxMARL environment rollouts. submitted by /u/Regular_Run3923 [link] [comments]

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