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Pulse · msn.com

msn.com · Nov 3

Taiwan's AI stock boom is making early-stage startups a harder sell

Taiwan's AI stock boom could be making it harder for young startups to win their first funding checks.

Pulse · jetstream.blog

jetstream.blog · Nov 3

Data#3 AI Practice: $3.4B Sales, FY27 Pods [2026]

Google has released the October 2026 edition of "Google AI Updates" (The latest AI news we announced in October 2026), summarizing the latest AI-related news announced last month in October 2026. The highlights for October 2026 include the general availability of the cutting-edge AI model "Gemini 4 Argon," the announcement of "Gemini 4 Carbon" (Google's most powerful AI model to date) just two weeks later, and "Genie 4," a real-time interactive world model with native audio support. Other…

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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

Oct 3

XTurnix: Large-Scale Self-Supervised Turn Control through Two-State Binary Decisions

General turn-taking behavior in real-time dialogue systems requires deciding whether to keep listening or start responding while listening, and whether to continue or stop while speaking. Existing turn detectors use heterogeneous, task-specific label spaces and are often…

arXiv

Oct 3

GlitchPatch: Repairing Glitch Tokens in Frozen Language Models via Local Retokenization

Glitch tokens are anomalous vocabulary entries that can cause large language models (LLMs) to produce outputs inconsistent with their inputs. Existing repair methods require access to model internals, making them impractical for frozen checkpoints. We investigate whether glitch…

arXiv

Oct 3

JASPER: Special Session on Joint Reliability And Security Assessment of SPlit Computing for Edge Robustness

Split Computing (SC) enables efficient deployment of Deep Neural Networks (DNNs) by partitioning inference between edge devices and cloud servers. However, intermediate feature representations are simultaneously exposed to hardware faults and adversarial attacks, which are…

arXiv

Oct 3

Detecting Defects that Matter: An Application-Driven Benchmark for Anomaly Detection in Manufacturing and Retail Logistics (VAND 4.0 Challenge)

Existing Anomaly Detection benchmarks are saturated and often unrealistic. As part of the VAND 4.0 Challenge, we introduce a hidden-test, application-driven benchmark across two deployment-critical domains: industrial manufacturing and retail logistics. In the Industrial Track…

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