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Pulse · freep.com

freep.com · Nov 3

Labarna AI Releases Two Films on Its 60-Day Build Model: MVP in Production on Day 30, Full Handover on Day 60

Pulse · cnbc.com

cnbc.com · Nov 3

Lovable CEO says AI is collapsing barriers to starting a business

Lovable CEO says AI is collapsing barriers to starting a business Lovable CEO Anton Osika joins Arjun Kharpal to discuss how AI is making it easier to build apps, start businesses and turn ideas into products. They explore vibe coding, AI agents, the future of developers and whether Europe can compete in the global AI race. 35:37 3 minutes ago Arjun Kharpal

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Top picks this weekarXiv

arXiv

Nov 2

Whitened Advantage Summation Policy Optimization: Correlation-Aware Aggregation for Multi-Reward Reinforcement Learning

Reinforcement learning for language models often combines several rewards by normalizing each one and adding them with fixed weights. When the rewards are correlated, the variation they share is counted once for every reward that carries it. In terms of the ZCA-decorrelated components of the reward signal, the weighted sum applies weights $Σ^{1/2} w$ rather than the chosen weights $w$, where $Σ$ is the reward correlation matrix. We study WAS-PO, which whitens the normalized advantages with $Σ^{-1/2}$ before adding them, so that the chosen weights apply to those components. We derive the scalarized objective whose gradient it follows when normalization statistics are held fixed, show that its weights on the raw rewards can be negative, give an exact two-reward condition under which the lighter-weighted reward is reversed, and give a loose bound on the error from estimating $Σ$ during training. On tool calling with Qwen2.5-1.5B-Instruct, WAS-PO reached a higher mean training reward than GDPO in all seven seeds with three rewards (3.8% higher) and all three seeds with four (3.9% higher). WAS-PO also replaces GDPO's per-prompt normalization with batch-level normalization, and we did…

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arXiv

Sep 20

Signal2Symbol: Neuro-Symbolic Temporal Reasoning for Explainable Physiological Time-Series Anomaly Detection

Physiological time series such as electrocardiograms (ECG) and electroencephalograms (EEG) exhibit complex temporal structure, substantial acquisition variability, and a strong need for transparent decision-making. Although deep models can achieve high detection performance,…

arXiv

Sep 20

Pay More Attention To Text In High-Resolution MLLMs

Failures of high-resolution MLLMs are commonly attributed to a visual problem, motivating zooming, cropping, and related visual interventions to recover fine-grained evidence or suppress interference. Yet recent studies suggest that relevant visual evidence is already encoded…

arXiv

Sep 20

CE$^4$L: Continual Ego, Exo, and Ego-Exo Learning

Perception for embodied agents is video-based, often multi-view (ego, exo, or both), and inherently continual, with simultaneous task and viewpoint shifts. Yet continual learning (CL) remains dominated by exo-only recognition tasks, obscuring behavior under these real-world…

arXiv

Sep 20

BabelArena: A Large-Scale Multilingual Benchmark for LLM Agents

Large language model (LLM) agents increasingly execute multi-step workflows through tool use and interaction with users and environments. However, current agent evaluations are largely English-centric, limiting our understanding of agent capabilities in multilingual settings.…

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