A survey in Artificial Intelligence Review categorises narrative theory-driven large language model methods and finds generation tasks lag behind understanding tasks.
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.
SHENZHEN, China, Oct. 30, 2026 /PRNewswire/ -- 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 ...
/PRNewswire/ -- Tencent today unveiled the enterprise capabilities of its AI office productivity agent, WorkBuddy and previewed the upcoming general release of...
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…
In Nanbeige4.3, we take a foundational step toward pushing the boundaries of lightweight agents. Our strategy centers on building a high-capacity teacher agent first, which will subsequently anchor the distillation of advanced agentic capabilities into smaller models.
Ryan is joined by Charity Majors, co-founder and CTO at Honeycomb, for a conversation about applying the concept of immutable infrastructure to software development to treat code like cattle instead of pets, shifting the paradigm of system specifications to accommodate the…
Multi-view clustering (MVC) aims to uncover latent cluster structures by exploiting complementary information from multiple views. Despite substantial progress in clustering performance, fairness remains an important concern when MVC is applied to socially sensitive scenarios.…
Fine-grained mixture-of-experts (MoE) architectures have become a mainstream design for open-weight LLMs, with hundreds of experts and increasingly many selected per token. This shift makes dynamic expert pruning an attractive route to cheaper inference. Yet existing evidence…
Evaluation scores are often validated by their agreement with inexpensive proxy labels. When the score and the proxy are computed from the same text span, however, that agreement can arise from surface evidence the two share rather than from the semantic construct the proxy is…
Runtime traces can appear transparent, but a closed-loop policy determines which states are visited and which failures become visible. We study a simulated hotel-pricing agent mapping time, inventory, and market state to discrete price actions under varying demand regimes. A…