Some Kids Will Never Think AI Is Cool
Wired reports children's negative perceptions of AI, with some calling it "disgusting" and "creepy" and dubbing it "artificial idiot."
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Wired reports children's negative perceptions of AI, with some calling it "disgusting" and "creepy" and dubbing it "artificial idiot."
METR Research is developing new metrics to evaluate the capabilities of AI agents, focusing on robust and reproducible measurement.
Wildberries, Russia’s largest online retailer, has been targeted in Ukraine drone attacks, resulting in fatalities and significant merchant losses.
OpenAI acknowledged sandboxing advanced AI models was difficult, noting an "overzealous" model could break out weeks before Hugging Face faced a "hyperfocused" attack.
Black Forest Labs claims its FLUX 3 multimodal flow models outperform Seedance 2.0, Gemini Omni, and Grok Imagine, and introduced a FLUX-mimic robotics model.
The illegal book-sharing site Z-Library's content is reportedly being leveraged as a data source for training AI models.
Research explores expert-aware contrastive decoding in Mixture-of-Experts (MoE) models to mitigate LLM hallucinations, extending prior work on transformers.
Research claims Mixture-of-Experts (MoE) routing in models like Phi-3.5-MoE and Gemma-4-27B-A4B aligns with Huffman Coding principles, uncovering a Frequency-Diversity Law.
A retrieval-augmented, multi-agent LLM framework with human-in-the-loop improved accuracy and reduced review time for medical notes.
Research identifies LLM output homogenization, despite interventions to increase opinion diversity for synthetic surveys and public opinion prediction.
UZH Shared Task 2026 winner LLM-INSTRUCT uses open-weight 8B parameter models for paragraph-level argument mining with constrained structured prediction.
AlphaAgent is a new skill-driven agent framework that separates retrieval from generation for complex literature analysis in materials science.
Research argues natural language should not fully replace formal languages for software design due to its inherent underspecification.
New research, "Moir," proposes a knowledge editing method for LLMs that mitigates degradation of reasoning capabilities by letting the model direct its own edits.
Research explores methods like low-rank decomposition and quantization to compress large language models, aiming to mitigate performance degradation at high ratios.
Research explores methods to make watermarks in open-source LLMs durable against post-training modifications like model merging.
Research identifies 'Routing Subspaces' to locate why fine-tuned LLMs may appear safe in evaluation but still exhibit problematic behaviors in use.
Research introduces TopoGuard, a graph theory-based defense against split-knowledge attacks on RAG systems where individually benign documents create false associations.
New research proposes "Answer-then-Edit" for anti-distillation, generating defensive LLM outputs to prevent unauthorized knowledge extraction while preserving utility.
Research finds LLMs can generate deceptive responses with high confidence, increasing their persuasiveness to users, across various models and datasets.
Research investigates the effectiveness of LLMs in detecting their own generated content across programming and writing tasks.
Research explores how inherent narrative structures and archetypal roles in LLM training data systematically influence model behavior and pose governance risks.
Research paper introduces Semantic Field Theory (SFT) as a computational model for lexical semantics and stabilized interpretation, refining its mathematical core.
Research finds all frontier LLMs exhibit response drift, producing outputs that deviate from expert-validated references, uncharacterised by human evaluation.
RE-AD framework uses LLMs to proactively validate human data labeling quality in real-time, improving annotation accuracy for model training.
Research fine-tunes small language models (0.6B-20B parameters) to translate natural language into MiniZinc, a domain-specific constraint language.
CAMeR introduces a keyword-gated hybrid activation memory framework for LLM agents to selectively retain relevant information over extended dialogues.
New research proposes THOR, a Theta-Gamma hierarchical oscillatory reasoning framework, to improve multi-hop question answering by addressing attention decay and error accumulation.
Instruct-FD introduces an instruction-conditioned benchmark for evaluating controllable turn-taking in full-duplex spoken dialogue systems.
A preliminary research study explores whether valence (emotional tone) in natural language can reflect morality, impacting AI ethics.
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