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Pulse · forbes.com

forbes.com · Oct 30

Token Resistance: Overcoming Fear And Doubt In The AI Agent Era

Agentic transformation in the business has implications far beyond simply employing agents as personal assistants.

Pulse · martechcube.com

martechcube.com · Oct 30

Cresta Opens AI Agent Platform With End-to-End Developer Self-Service

Businesses can now build and optimize customer-facing AI Agents with enterprise-grade developer tools and infrastructure on the Cresta platform. Cresta, the AI operating system for customer experience, today announced end-to-end self-service for developers building AI agents in-house or for their clients. Teams can now build, test, deploy, and optimize customer-facing AI agents on the Cresta

Pulse · finance.yahoo.com

finance.yahoo.com · Oct 30

Broadcom Stock Outlook Hinges On AI Chip Growth And Anthropic Partnership

Broadcom Inc. recently reported sharp growth in AI semiconductor revenue, expanded custom accelerator work with Anthropic and Meta, and shipped 89 security fixes in TrueSource Spring Enterprise while rolling out new application security and patch automation capabilities.

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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…