AI-powered robotics in Europe: Live demonstrations and strategic debate
The European Parliament will host a strategic debate on AI-powered robotics in September 2026, featuring live demonstrations.
European Commission Digital Strategy·24 Jul
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The European Parliament will host a strategic debate on AI-powered robotics in September 2026, featuring live demonstrations.
AMD and Alphabet stock performance indicates investor concerns about an AI bubble, despite positive earnings reports from chip and tech giants.
A guest columnist advises enterprise engineering leaders to build adaptable, vendor-agnostic infrastructure to enable switching between models.
Samsung and SK Hynix are expected to finalize large chip supply deals with US tech companies during President Lee Jae Myung’s Silicon Valley visit.
Israel and the UK have appointed dedicated AI ministers, aiming to bolster national AI competitiveness against the US and China.
Intel's revenue is increasing, despite ongoing supply chain challenges impacting overall growth, hinting at a potential market comeback.
EU escalates probe against TikTok for failing to protect teen user safety and privacy under content moderation rules.
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 factors impacting the detection of deceptive outputs from LLMs, noting current probes fail in out-of-domain scenarios.
Research benchmarked five LLMs on multi-sensor physical hazard assessment, finding all consistently failed to issue precautionary warnings.
Research identifies epanorthosis, a rhetorical self-correction, as a systematic overuse in LLM text due to training data and RLHF.
Research proposes using reinforcement learning to detect user interface (UI) principle violations, including accessibility and poor visual hierarchy, in LLM-generated front-end code.
New research proposes M"obius RoPE, an anti-periodic positional encoding method to improve in-context retrieval reliability in large language models.
Research identifies "directional hallucinations" and ideological drift in LLMs when answering political questions, using a new measurement framework.
PersonaTrail introduces a new benchmark for personalized web agents, evaluating their ability to infer context from user browsing histories.
MedGame introduces an LLM-powered framework to transform static clinical cases into interactive, decision-centered storytelling games for medical education.
GenDB, a generative query engine using LLM agents, is demonstrated to automatically generate customized query processing code, aiming to reduce engineering effort.
Researchers developed news-crawler-LM, a small long-context model for extracting structured content from diverse and complex news page layouts.
Telco-GAIA introduces a bilingual, multi-modal benchmark for evaluating tool-using agents on real-world telecom data with complex reasoning.
Researchers propose "Refusal-Gated Decoding" to maintain LLM refusal behaviors when using high-temperature sampling for output diversity.
NVIDIA-labs introduces Object-Oriented Agents (NOOA), a Python framework for building reliable AI agents by representing agents as Python objects.
Research introduces WaveformQA, a new benchmark to evaluate LLMs' temporal reasoning over digital waveform data, addressing a design verification gap.
ARCO proposes adaptive, co-evolving, natural-language rubrics for multi-step LLM agent rewards, addressing limitations of scalar and static rubric-based methods.
Researchers introduced progressive cramming, a method to compress sequences into learned embeddings with near-perfect reconstruction by iteratively growing the token budget.
REFACT proposes an adaptive method for LLMs to restate facts in their chain-of-thought reasoning, aiming for compact, faithful, and context-aligned outputs.
Research explores using transformer-assisted LLMs for source code summarisation to improve secure software development lifecycle maintenance.
VibeVoice-ASR-BitNet introduces a highly compressed ASR model using INT8 and BitNet-style ternary quantization for edge CPU real-time inference.
Research explores open-weight LLMs for agentic coding on local, sensitive data, bypassing cloud transmission restrictions.
Research explores creating dynamic and physically realistic 4D virtual worlds from natural language using generative models, moving beyond manual graphics.
OpenForgeRL is an open-source framework designed to train AI agents that use complex, multi-turn inference harnesses like Claude Code or Codex.
Euclid-MCP proposes a standardized protocol server for integrating LLMs with Prolog-based symbolic reasoning, aiming for reliable logical outputs.
LegalCiteTrust benchmark evaluates citation trustworthiness in LLM-generated long-form legal research by identifying misrepresentations.
Research introduces HiMe, a real-time, self-hosted, open-source personal agent platform for health insights from wearable data using LLM agents.
CMI-Mem proposes an RL-based memory manager for agent systems, using a hybrid reward combining QA correctness and intrinsic Conditional Mutual Information.
Agentic Memory (AgeMem) proposes a unified framework for LLM agents to manage long-term and short-term memory, addressing context window limitations.
A new distillation framework, SCoRe, improves smaller LLM agents' multi-step reasoning by generating student-centric trajectories to narrow the performance gap.
Research introduces DatedGPT, 1.3B-parameter LLMs pretrained on time-partitioned data to prevent lookahead bias in forecasting tasks.
Research identifies LLM output homogenization, despite interventions to increase opinion diversity for synthetic surveys and public opinion prediction.
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.
Research finds language models widely hallucinate in chemical reasoning, often producing correct answers with fabricated intermediate steps.
Research finds open-weight LLMs exhibit demographic disparities; Black-associated names lead to higher first-token entropy and more diverse continuations.
Researchers introduced Rushes, a human preference dataset for pluralistic alignment collected from interactive AI-generated narratives.
Research argues natural language should not fully replace formal languages for software design due to its inherent underspecification.
FlyRoute, an arXiv paper, proposes a self-evolving framework for agent profiling that uses real traffic to dynamically update agent capabilities for task routing.
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