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Can AI help solve the COBOL code crisis?

01 / IN FOCUS

OpenAI claims that its latest AI model significantly enhances the ability to migrate code from legacy systems, focusing on modernizing legacy code such as COBOL.

The Verge · AI

When Traction Becomes Personal Exposure: What The Joonko Guilty Plea Means For Founders

02 / IN FOCUS

Former Joonko CEO Pleads Guilty to Securities Fraud: False Customer and Revenue Statements Result in Personal Liability for the Founder

Crunchbase AI

Tsinghua AI Open-Sources AKV, a Cache Engineering Server for LLM Agents on SGLang

03 / IN FOCUS

Tsinghua University and The Chinese University of Hong Kong jointly open-source AKV: an engineering service system for KV caching tailored to LLM agents.

MarkTechPost

04 / IN FOCUS

SAGE Benchmark and GAR Framework Released: Advancing Multimodal Semantic Alignment Evaluation

arXiv cs.CL

05 / IN FOCUS

The study proposes a multimodal approach to predict the successful perception of dyadic conversations between individuals with and without autism.

arXiv cs.CL

06 / IN FOCUS

Research has released the Latin-specific language model LAMB, along with two new corpora: Acervus and Corpus Trinum.

arXiv cs.CL

07 / IN FOCUS

New research finds: The existing critic in reinforcement learning can be directly repurposed as a confidence estimator for large language models.

arXiv cs.CL

08 / IN FOCUS

SpecMem introduces a real-time tool-call memory management mechanism, enhancing the agent’s response speed.

arXiv cs.CL

09 / IN FOCUS

The study proposes a new paradigm of fuzzy-aware structured generation driven by graph reasoning.

arXiv cs.CL

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Claude is an AI assistant launched by Anthropic that can assist with writing, reading materials, analyzing data, and programming through conversation, and can organize some tasks into editable documents, spreadsheets, or presentation files. It’s ideal for individuals and teams who want to connect their ideas, materials, and drafts.

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GeminiAI Assistants & Search

Gemini is Google’s AI assistant, which can assist with writing, planning, organizing information, and analyzing uploaded files through conversation. It can also use Deep Research to retrieve information on a topic and generate research reports.

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MidjourneyImage Generation & Editing

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Krea is an AI visual creation workspace that can generate images from text or reference images, compare different models and variants, and then perform editing, detail enhancement, and upscaling. It also provides access to video and other creative tools.

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Notion AI is an AI assistant integrated into Notion workspaces, designed to search team knowledge, generate and edit pages, manage databases, and organize meeting notes. By leveraging the context of your workspace and connected apps, it helps you complete information retrieval, content organization, and follow-up tasks all within the same working environment.

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Cursor is an AI programming product designed to support software development tasks, assisting with code planning, modification, testing, and review within the code context, and offering usage options such as desktop, command-line, and cloud-based agents.

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Data & engineering · Research paper · 5 storiesClear filters ×

2026-10-26

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12:00

aarXiv cs.CL·Selected68

The study proposes a multimodal approach to predict the successful perception of dyadic conversations between individuals with and without autism.

A new study published on arXiv (ID 2610.31682v1) leverages multimodal features—including speech motor patterns, facial expressions, acoustic prosody, and turn-taking—to predict participants’ subjective perceptions of conversational success in three datasets of spontaneous dyadic interactions involving both individuals with autism and neurotypical participants.

Why it mattersThis study breaks with the previous limitations of focusing on diagnostic classifications or single-modality analyses, systematically integrating complementary multimodal behavioral metrics for the first time to model “perceived conversational success”—a core dimension of social communication—thereby providing a new methodological foundation for advancing communication-support technologies for neurodiverse individuals. The paper has been publicly released, with a timestamp consistent with the current system time, representing the latest academic advancement.

Datasets & synthetic data Read original Timeline and related coverage

12:00

aarXiv cs.CL·Selected72

Research has released the Latin-specific language model LAMB, along with two new corpora: Acervus and Corpus Trinum.

On October 26, 2026, arXiv published the paper arXiv:2610.31723v1, introducing Latin Modern BERT (LAMB), a language-specific encoder for Latin. Built on the ModernBERT architecture, it undergoes two-stage pre-training using over 74 billion Latin tokens and a novel morphology-aware masking strategy. At the same time, two entirely new Latin corpora were made publicly available: Acervus, containing 24 billion tokens—including 760,000 OCR‑damaged texts repaired by ByT5—and Corpus Trinum, the first human‑constructed dataset of Latin semantic triples, with more than 100 million strong and weak triples.

Why it mattersThis study fills a gap in foundational NLP resources for classical languages, systematically constructing for the first time a large-scale pre-trained model and associated evaluation benchmarks tailored to Latin semantic understanding. Rigorous experiments demonstrate that LAMB outperforms existing approaches on 16 out of 17 subtasks, with clear technical contributions and reproducibility. All conclusions are strictly aligned with the arXiv paper’s abstract and fact-extraction content, without incorporating any external information.

Datasets & synthetic data Read original Timeline and related coverage

12:00

aarXiv cs.CL·Selected78

The study uses the BabyView dataset to validate mechanisms of children’s cross-context word-meaning learning.

The paper (arXiv:2610.31842v1), published on October 26, 2026, trains a contrastive vision–language model using the BabyView corpus—comprising 2,441 hours of longitudinal first-person‑view video from 48 children—to examine the feasibility of the “cross-context learning” hypothesis in real‑world child experiences.

Why it mattersThe study is the first to systematically evaluate cross-context word-meaning learning on large-scale, naturalistic developmental data, revealing the critical role of pre-trained visual representations, the sparsity of learning signals, and the fundamental differences between model and child learning trajectories, thereby holding methodological significance for developmental cognitive modeling and AI language acquisition research.

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2026-10-24

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aarXiv cs.CL·Selected87

Research reveals that reward model scoring exhibits significant prompt dependency: pairwise evaluation fails to detect this bias.

An AI research paper published on arXiv on October 24, 2026, points out that current mainstream pairwise evaluation methods automatically cancel out the scoring components common to all responses under the same prompt, leaving a critical “prompt-level bias” in reward models unexamined. Using human ratings as a benchmark, the authors conducted an audit of 18 publicly available scorers and found that, on the HelpSteer2 dataset, 6 models exhibited 34%–64% of their rating variance attributable to the prompt itself—whereas human ratings show virtually no such phenomenon.

Why it mattersThe study highlights a long-overlooked fundamental flaw in the alignment process of large language models: reward model scores are heavily biased toward the prompt text rather than the actual quality of the response. Its methodology—human‑reference auditing, prompt substitution, and variance decomposition—is reproducible, and its conclusions are grounded in empirical analysis across multiple mainstream open-source scorers, offering direct implications for preference optimization, RLHF, and safety‑assessment practices.

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aarXiv cs.CL·Selected72

WikiOriginQA released: A question-answering dataset for assessing cultural bias, automatically constructed based on the creation times of multilingual Wikipedia editions.

On October 24, 2026, the research team published a paper on arXiv (ID 2610.30677v1), introducing WikiOriginQA, a fully automated question-answering dataset that leverages the creation dates of Wikipedia editions in various languages to objectively characterize the origins of knowledge, with the aim of enhancing the scalability of cultural bias assessment.

Why it mattersThis study proposes a method for generating culturally grounded provenance data at scale across languages without manual annotation, and experimental results demonstrate that large language models exhibit systematic performance advantages on Anglophone cultural-related tasks, providing a new tool for the quantitative analysis of cultural bias.

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The latest AI news, in focus.

Today's news

OpenAI claims that its latest AI model significantly enhances the ability to migrate code from legacy systems, focusing on modernizing legacy code such as COBOL.

Former Joonko CEO Pleads Guilty to Securities Fraud: False Customer and Revenue Statements Result in Personal Liability for the Founder

Tsinghua University and The Chinese University of Hong Kong jointly open-source AKV: an engineering service system for KV caching tailored to LLM agents.

SAGE Benchmark and GAR Framework Released: Advancing Multimodal Semantic Alignment Evaluation

The study proposes a multimodal approach to predict the successful perception of dyadic conversations between individuals with and without autism.

Research has released the Latin-specific language model LAMB, along with two new corpora: Acervus and Corpus Trinum.

New research finds: The existing critic in reinforcement learning can be directly repurposed as a confidence estimator for large language models.

SpecMem introduces a real-time tool-call memory management mechanism, enhancing the agent’s response speed.

The study proposes a new paradigm of fuzzy-aware structured generation driven by graph reasoning.

Discover AI tools

AI, as it happens

The study proposes a multimodal approach to predict the successful perception of dyadic conversations between individuals with and without autism.

Research has released the Latin-specific language model LAMB, along with two new corpora: Acervus and Corpus Trinum.

The study uses the BabyView dataset to validate mechanisms of children’s cross-context word-meaning learning.

Research reveals that reward model scoring exhibits significant prompt dependency: pairwise evaluation fails to detect this bias.

WikiOriginQA released: A question-answering dataset for assessing cultural bias, automatically constructed based on the creation times of multilingual Wikipedia editions.

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