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Pulse · msn.com

msn.com · Oct 31

Elizabeth Warren slams Trump's voluntary AI safety deal with tech CEOs, says Republicans aren't doing enough to regulate AI: 'Not lifting a finger'

Sen. Elizabeth Warren (D-Mass.) has renewed her criticism of the Donald Trump administration's approach to artificial intelligence (AI) regulation, arguing that voluntary safety commitments from major tech companies are no substitute for enforceable government oversight.

Pulse · moneycontrol.com

moneycontrol.com · Oct 31

Cybersecurity gets an AI reality check as PHP, LiteLLM and eMudhra expose security gaps

Click here to track and Analyse your mutual fund investments, Stock Portfolios, Asset Allocation. Start tracking your investments in stocks, mutual fund, gold, bank deposits, property and get all your details about your investments in a single place with Moneycontrol�s Portfolio Manager.

Pulse · datacenters.economictimes.indiatimes.com

datacenters.economictimes.indiatimes.com · Oct 31

DSCI releases India-specific agentic AI liability guide

The Data Security Council of India (DSCI) has published an India-focused guide on artificial intelligence (AI) liability for agentic AI systems. This guide clarifies how responsibility is allocated across the AI value chain if an agent operates autonomously or deviates from its tasks.

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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 30

TopTimeNet: Topologically-assisted time-series classification model

Distinguishing periodic from chaotic dynamics in a time series is a fundamental challenge in both physics and engineering. Yet, end-to-end learned architectures must discover both a representation and a decision boundary from data, at substantial cost. We introduce TopTimeNet,…

arXiv

Sep 30

Pseudo-Label-Triggered Retraining from Forecast Errors for Online Time Series Forecasting

Real-world time series forecasting systems operate under non-stationary data streams, where forecasting performance may degrade over time. Although retraining can recover the performance, it incurs non-trivial computational and operational costs. Under limited deployment…

arXiv

Sep 30

Safety of Latent Communication in Multi-Agent Systems

Latent communication enables multi-agent systems to exchange information directly in internal representation space, reducing the token, computation, and latency overhead of text-based communication. To this end, lightweight trainable links are introduced to map the sender's…

arXiv

Sep 30

Context Injection and Long-Horizon Reliability: Separating Duplicate Recognition, Local Processing, and Counter Tracking

Long-horizon failures of language models are usually measured as end-task success or as a horizon length. We instead score, step by step, three components of a 400-step ledger task in which every step is easy in isolation and the model's own answers are re-injected into its…

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