1100 stories tagged Research · page 7 of 46
Researchers introduced SCOPE, a benchmark for creating schema graphs from raw text, and SCION, an auditable reference pipeline. These tools aim to improve how machines extract structured information from unstructured data.
Researchers found that using official conference guidelines for AI-based peer review produces results most consistent with human judgments. This suggests that refined evaluation criteria from conference practice enhance AI review quality.
Researchers introduced three new attention-guided strategies—Attention-JSD, Attention-Entropy-Max, and Attention-Entropy-Min—to improve the factual accuracy of large language models by leveraging self-attention mechanisms for better layer selection during contrastive decoding.
Researchers introduced LENS, a benchmark that tests whether AI models can suppress disinformation-aligned narratives. The study found that current unlearning techniques fail to fully eliminate harmful frames, especially in abstract and contrastive contexts.
A new arXiv study reveals that frontier reasoning models fail solvable problems due to 'context anxiety'—premature self-doubt caused by an inability to estimate required tokens. This leads to significant efficiency losses and shorter, less accurate responses.
A new OpenAI field report details how scientists use AI coding agents to modernize scientific computing, accelerating software development and discovery in genomics and beyond.
Researchers introduced GAND, a gender-ambiguous natural data benchmarking resource for machine translation, designed to help AI systems translate sentences without clear gender cues more accurately and inclusively.
Researchers propose FrED, a probabilistic framework that traces AI training data origins using knowledge graphs without needing access to model weights or internal architecture. This black-box approach could improve transparency and accountability in generative AI.
Researchers introduced AutoThinkSQL, a framework that trains AI models to decide when to use complex reasoning and when to skip it for Text-to-SQL tasks, reducing inference time on simple queries without sacrificing accuracy on complex ones.
A new arXiv study reveals that frontier AI models use semantically irrelevant filler tokens to improve reasoning accuracy by up to 13 percentage points, raising transparency concerns for AI safety.
A new study on ArXiv shows that a two-stage Gemini AI pipeline can detect internal inconsistencies in 3,000 MIMIC-IV-Note discharge summaries, but recurring failure modes limit its reliability for automated clinical use.
A new study combining speech analysis with LLM-based transcript analysis shows improved prediction of romantic attraction in speed dating, but the effect is conditional on context, not universal.
Researchers introduced ADAGE, a language-agnostic pipeline that creates AI benchmarks in native languages without relying on translations. Validated with benchmarks in Arabic, Amharic, and Japanese, ADAGE combines native-speaker curation with LLM-assisted generation to test culturally grounded analogical reasoning.
Researchers introduced TILT, a training-free framework that enhances AI-generated images by ensuring all parts of complex compositional prompts are accurately represented. The method uses a model-intrinsic reward to guide diffusion models toward more faithful depictions of multi-concept scenes.
A new study identifies the Hard Decision Layer (HDL), a natural architectural point in transformer models where answer rankings stabilize. The HDL was found consistently across Qwen, Llama, Granite, and Mistral models and is invariant to fine-tuning, offering a new path to more reliable AI decision-making.
A new ArXiv paper introduces 'Coupled Hierarchical Search over Topology and Execution,' a method that automates the creation of structured workflows for Large Language Models (LLMs). This approach reduces the need for heavy offline training by dynamically linking subtask boundaries with execution outcomes.
Researchers have developed a runtime-aware framework that predicts how long large language models (LLMs) will take to respond on heterogeneous edge devices like smartphones and IoT gadgets, achieving 95% accuracy by accounting for hardware, runtime backend, thermal variation, and prompt behavior.
Safe Superintelligence, founded by former OpenAI chief scientist Ilya Sutskever, has emerged from two years of stealth with a long-term partnership with Nvidia. The collaboration provides access to Nvidia's GPUs and software to accelerate research into safe, powerful AI systems.
Researchers introduced FlowEvo, a training-free framework that compiles successful AI agent traces into reusable skill records, enabling agents to improve efficiency and flexibility over time without additional data or computational resources.
Stanford researchers introduced AgentKVShift, a training-free technique that reduces the cost of AI memory systems by up to 90% by reusing key-value (KV) caches for structured memory units like summaries and tags, making AI agents significantly more efficient.
The Trump administration unveiled the first 'Genesis Mission' grants on Thursday, directing $5 billion toward hundreds of AI-driven science projects in an effort the White House has described as 'comparable in urgency and ambition to the Manhattan Project.'
A new arXiv study reveals that large language models produce homogenized opinions in tasks like synthetic surveys and public opinion prediction. Researchers identify which interventions actually increase diversity and which don't, offering a clearer path to more realistic AI-generated viewpoints.
A new ArXiv study decomposes the internal updates AI models undergo during preference-based fine-tuning (RLHF). By isolating spectral components of LoRA updates, researchers show these changes can be reorganized, recombined, and directly intervened on — making AI personalization and safety more transparent and controllable.
Researchers have developed a technique to make text watermarks in open-source LLMs survive model merging, a common post-training modification that previously removed such watermarks. The work, published on arXiv, addresses a key vulnerability in tracking AI-generated text.