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Pulse · msn.com

msn.com · Oct 30

Meet AJ Jain and Aditya Ketkar, BITS Pilani alumni who went door to door in California to raise $52 million for their AI startup

Broccoli AI, an AI startup co-founded by AJ Jain and Aditya Ketkar from BITS Pilani, develops innovative solutions for service businesses. The founders engaged with contractors across Northern California,

Pulse · calcalistech.com

calcalistech.com · Oct 30

AI agents violate their instructions in 40% of corporate tasks, study finds

An Eon study tested 19 models across simulated business environments and found that even the best performers failed to follow instructions in more than one in five runs. The most common failure was falsely claiming to have completed actions that systems had blocked.

Pulse · ca.finance.yahoo.com

ca.finance.yahoo.com · Oct 30

Tencent Cloud Accelerates International Growth, Advances Enterprise AI and Expands Global Partnerships

Tencent today unveiled the enterprise capabilities of its AI office productivity agent, WorkBuddy and previewed the upcoming general release of its Hy4 model and Hy4 Flash at the 2026 Tencent Global Digital Ecosystem Summit. Tencent Cloud's active overseas customer base has also doubled over the past year, while international demand for its AI products and services has grown by triple digits.

Pulse

Top picks this weekarXiv

arXiv

Oct 29

Elucidating the Design Space of LM Data Attribution

Data attribution methods for Language Models (LMs) are typically evaluated using downstream tasks like fact retrieval. However, existing evaluations vary multiple dataset properties simultaneously, making it impossible to isolate exactly why an attribution algorithm fails. To diagnose these failures, we explore the design space of LLM data attribution, with a primary focus on evaluation. We introduce the Controlled Attribution Benchmark (CATT) for fact tracing, in which we fine-tune LLMs on entirely fictitious knowledge, providing unambiguous ground truth for attribution. This synthetic setup allows us to systematically alter one dataset configuration at a time, such as document redundancy and lexical distractors. With this configurable evaluation, we demonstrate that redundancy and distractors consistently degrade attribution performance. Rather than proposing a new estimator, we examine two design choices that are underexplored: the loss used to compute gradients and how per-token attribution scores are aggregated into a document score. We find that the true causal evidence within a training document is highly localized, but standard methods dilute this evidence into noise by…

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arXiv

Sep 18

Periodic Neural Mapping for Unsteady Rotor-Blade Pressure and Aeroelastic Load Prediction

Accurate prediction of unsteady aerodynamic loads remains a major challenge in turbomachinery design. High-fidelity Computational Fluid Dynamics (CFD) simulations are expensive, while aeroelastic Quantities of Interest (QoI) depend sensitively on the temporal evolution of the…

arXiv

Sep 18

Predictive Suppression Layers for Communication-Efficient Spiking Neural Networks

Feedforward Spiking Neural Networks (SNNs) typically propagate every generated spike indiscriminately, disregarding whether the information is redundant from an information-theoretic perspective. This lack of selectivity induces high redundancy in inter-layer communication,…

arXiv

Sep 18

GestureFAR: Streaming Co-Speech Gesture Generation with Flow Autoregression

Generating natural co-speech gestures from streaming speech is essential for embodied conversational agents, where motion must be produced while a user is still speaking. Recent streaming gesture systems make online generation possible by autoregressing over discrete motion…

arXiv

Sep 18

Micro-Collaborative Poisoning: A Distributed Attack on RAG Systems

Retrieval-Augmented Generation (RAG) improves large language models by grounding outputs in external knowledge sources, but this dependency also creates a surface for poisoning attacks. This paper introduces Micro-Collaborative Poisoning, a distributed attack in which a false…

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