Qwen operates across multiple domains, including qwen.ai and chat.qwen.ai, indicating a robust infrastructure for AI-driven applications.
We Reverse-Engineered Docker Sandbox's Undocumented MicroVM API
Docker's undocumented MicroVM API enables the creation of isolated environments for running untrusted code, offering a significant security advantage over traditional containers by utilizing separate kernels for each microVM.
PopuLoRA: Co-Evolving LLM Populations for Reasoning Self-Play
PopuLoRA introduces a population-based asymmetric self-play framework for reinforcement learning with verifiable rewards (RLVR), enabling large language models (LLMs) to develop advanced reasoning skills through adaptive task generation and evaluation.
CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution [R]
CANTANTE optimizes agentic systems by addressing the credit assignment problem, allowing for automated learning of agent prompts from task rewards instead of manual tuning, enhancing system autonomy and reliability.
Can liveness detection models generalise to synthetic media generation techniques they were never trained on? [D]
Liveness detection models may struggle to generalize to new synthetic media generation techniques, as they were primarily trained on outdated datasets that do not reflect current advancements in deepfake technology.
OCTOPUS: Optimized KV Cache for Transformers via Octahedral Parametrization Under optimal Squared error quantization
OCTOPUS enhances KV cache efficiency by employing joint quantization of rotated coordinate triplets, optimizing memory usage in long-context autoregressive inference beyond previous codecs like TurboQuant and PolarQuant.
Vega: Zero-knowledge proofs for digital identity in the age of AI
Vega enables users to prove facts from government-issued credentials, such as age or professional status, without revealing the credential itself, ensuring privacy and security. This is achieved through zero-knowledge proofs generated in under 100 ms on standard devices, making it scalable for real-world applications like mobile driver’s licenses and the EU Digital Identity Wallet.
Learnings from 100K lines of Rust with AI (2025)
AI coding agents enabled the development of a Rust-based multi-Paxos consensus engine, modernizing Azure's Replicated State Library (RSL) and achieving a throughput increase from 23K ops/sec to 300K ops/sec in just three weeks.
NVIDIA GTC Taipei at COMPUTEX: Live Updates on What’s Next in AI
NVIDIA GTC Taipei at COMPUTEX showcases cutting-edge advancements in AI, featuring live demos and a keynote by CEO Jensen Huang on June 1, 2026, emphasizing the convergence of developers and industry leaders to explore innovations in AI factories and autonomous systems.
MagenticLite, MagenticBrain, Fara1.5: An agentic experience optimized for small models
MagenticLite integrates MagenticBrain and Fara1.5 to create a seamless agentic experience optimized for small models, enabling efficient task execution across browsers and local systems.
Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL [R]
OpenAI claims a general-purpose reasoning model found a counterexample to Erdos's unit-distance bound [D]
OpenAI's reasoning model has reportedly disproven Erdős’s conjectured upper bound of n^{1+O(1/log log n)} in the planar unit-distance problem, suggesting the existence of finite point sets with more than n^{1+δ} unit distances for some fixed δ > 0.
ACL-Verbatim: hallucination-free question answering for research
VerbatimRAG effectively mitigates hallucinations in AI-assisted research by mapping user queries to verbatim text spans in the ACL Anthology, enhancing the reliability of information retrieval.
Single-Pass, Depth-Selective Reading for Multi-Aspect Sentiment Analysis
DABS introduces a single-pass inference framework for Aspect-Term Sentiment Analysis (ATSA), allowing for reusable, depth-ordered representations that enhance efficiency without sacrificing expressiveness.
Finding the Correct Visual Evidence Without Forgetting: Mitigating Hallucination in LVLMs via Inter-Layer Visual Attention Discrepancy
LVLMs often hallucinate due to insufficient attention to correct visual evidence, leading to a gradual loss of this information during text generation, which our study addresses through a novel method called Inter-Layer Visual Attention Discrepancy (ILVAD).