Funding & M&A C

The paper introduces P3B3, an expert-curated benchmark and framework for measuring European versus Brazilian Portuguese variety bias in LLMs. It reports most models lean strongly toward pt-BR and argues for more balanced multilingual representation.

The paper presents UNIATTACK, an adversarial testing framework that systematically builds effective black-box attack prompts on LLMs from a defense-oriented perspective. Unlike static templates or model-specific tuning, it extracts minimal but high-impact features from diverse existing attacks and optimizes them.

An arXiv paper reportedly examining preference collapse in multimodal evaluators and its cross-modal contagion within self-evolving agent systems. The source excerpt was unavailable (content filter), so this summary is based on the title only; see the original for methods and findings.

Note: the abstract was unavailable, so this is summarized neutrally from the title alone. The paper proposes SCAR, a 'semantic continuity-aware retrieval' method aimed at efficient context expansion in retrieval-augmented generation (RAG). Specific mechanisms and evaluation results cannot be confirmed from the title.

This survey reviews the emerging field of Islamic LLMs and trustworthy Islamic AI, spanning Arabic NLP, Qur'anic question answering, knowledge benchmarks, retrieval-augmented generation, and legal reasoning. It argues that Arabic fluency alone is insufficient, and that reliable systems need curated sources, verification modules, and citation-aware generation.

This arXiv paper argues that standard machine-translation (MT) metrics assess quality intrinsically and miss the downstream consequences of translation errors. Under a static regime, the authors propose an entity-counting task probing referential consistency and show high intrinsic MT quality does not reliably predict downstream discourse success. Under an interactive regime, they use the goal-oriented multi-agent Welfare Diplomacy game as a probe.

This arXiv study tests whether LLM agents can analyze ubiquitous time series data used in finance, healthcare, and environmental monitoring. Comparing three approaches—raw numerical data, the LLM as a coding agent, and a combination—the authors find that agents with code access can outperform models processing raw data by up to 10%, though even the best agent still answers roughly 22-34% incorrectly.

Cohere announced it will move to a larger London office at 100 New Oxford Street, nearly tripling its UK footprint. The expansion backs the city's AI talent and R&D base and supports growing demand for secure, enterprise-grade sovereign AI across the UK and Europe.

In a new essay, Y Combinator co-founder Paul Graham examines how to earn a billion dollars, arguing the most reliable path is founding a fast-growing startup that makes something people genuinely want. He frames wealth creation in terms of scale, market choice, and entrepreneurial ambition, in his characteristically plain-spoken style.

Simon Willison's blog features a quotation from Andrew Singleton. Based on its tags, the post touches on Meta, software engineering, and neural networks; see the original post for the full quote and context.

The work studies which uncertainty directions a model must cover in affine robust optimization defined by a finite dictionary and budget. It proposes a sparse design selecting the directions that matter.

Allen Institute for AI published olmo-eval, an evaluation workbench for the model development loop. The tool appears to support continuous evaluation of models during training, building on the team's OLMo open-model development work.

Modern image classifiers widely use global average pooling followed by a linear head. The paper shows this linearity makes GAP-based classifiers behave as multi-instance learners, prompting a rethink of global average pooling.