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Last updated: 2026-02-07 | Server uptime: 99.9% ⚡
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⚡ BREAKTHROUGH
🎯 Context scaling • Memory efficiency • Model improvements
💬 "a model with 10M context size will have a memory approaching that of a person"
• "The fact that 10x context only costs ~30% decode speed is the real headline here"
🛠️ TOOLS
"I’ve repeatedly run into the same issue when working with ML / NLP systems (and more recently LLM-based ones):
there often isn’t a single *correct* answer - only better or worse behavior - and small changes can have non-local effects across the system.
Traditional testing approaches (assertions, s..."
🔬 RESEARCH
via Arxiv
👤 Jian Chen, Yesheng Liang, Zhijian Liu
📅 2026-02-05
⚡ Score: 7.3
"Autoregressive large language models (LLMs) deliver strong performance but require inherently sequential decoding, leading to high inference latency and poor GPU utilization. Speculative decoding mitigates this bottleneck by using a fast draft model whose outputs are verified in parallel by the targ..."
🔬 RESEARCH
via Arxiv
👤 Jian Chen, Zhuoran Wang, Jiayu Qin et al.
📅 2026-02-05
⚡ Score: 6.9
"Large language models rely on kv-caches to avoid redundant computation during autoregressive decoding, but as context length grows, reading and writing the cache can quickly saturate GPU memory bandwidth. Recent work has explored KV-cache compression, yet most approaches neglect the data-dependent n..."
🔬 RESEARCH
via Arxiv
👤 Yuxing Lu, Yucheng Hu, Xukai Zhao et al.
📅 2026-02-05
⚡ Score: 6.8
"Multi-agent systems built from prompted large language models can improve multi-round reasoning, yet most existing pipelines rely on fixed, trajectory-wide communication patterns that are poorly matched to the stage-dependent needs of iterative problem solving. We introduce DyTopo, a manager-guided..."
🌐 POLICY
"Sam Altman: ["Thank you for being such a pro-business, pro-innovation President. It's a very refreshing change...The investment that's happening here, the ability to get the power of the industry back... I don't think that would be happening without your leadership."](
https://x.com/RapidResponse47/s..."
🎯 Corruption & Cronyism • Political Double Standards • Wealth Concentration
💬 "This admin is buying businesses and bullying others"
• "The double standard is staggering"
📡 AI NEWS BUT ACTUALLY GOOD
The revolution will not be televised, but Claude will email you once we hit the singularity.
Get the stories that matter in Today's AI Briefing.
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🔬 RESEARCH
via Arxiv
👤 Lizhuo Luo, Shenggui Li, Yonggang Wen et al.
📅 2026-02-05
⚡ Score: 6.6
"Diffusion large language models (dLLMs) have emerged as a promising alternative for text generation, distinguished by their native support for parallel decoding. In practice, block inference is crucial for avoiding order misalignment in global bidirectional decoding and improving output quality. How..."
🔬 RESEARCH
via Arxiv
👤 Xianyang Liu, Shangding Gu, Dawn Song
📅 2026-02-05
⚡ Score: 6.6
"Large language model (LLM)-based agents are increasingly expected to negotiate, coordinate, and transact autonomously, yet existing benchmarks lack principled settings for evaluating language-mediated economic interaction among multiple agents. We introduce AgenticPay, a benchmark and simulation fra..."
🔬 RESEARCH
via Arxiv
👤 Tiansheng Hu, Yilun Zhao, Canyu Zhang et al.
📅 2026-02-05
⚡ Score: 6.5
"Deep research agents have emerged as powerful systems for addressing complex queries. Meanwhile, LLM-based retrievers have demonstrated strong capability in following instructions or reasoning. This raises a critical question: can LLM-based retrievers effectively contribute to deep research agent wo..."
🔬 RESEARCH
via Arxiv
👤 Haozhen Zhang, Haodong Yue, Tao Feng et al.
📅 2026-02-05
⚡ Score: 6.5
"Memory is increasingly central to Large Language Model (LLM) agents operating beyond a single context window, yet most existing systems rely on offline, query-agnostic memory construction that can be inefficient and may discard query-critical information. Although runtime memory utilization is a nat..."
🛡️ SAFETY
🎯 Game vs. coding agents • Security vs. user experience • Sandbox security
💬 "The players in competitive games don't write code. Coding agents do."
• "People want convenience more than they want security."
🔬 RESEARCH
via Arxiv
👤 John Kirchenbauer, Abhimanyu Hans, Brian Bartoldson et al.
📅 2026-02-05
⚡ Score: 6.4
"Existing techniques for accelerating language model inference, such as speculative decoding, require training auxiliary speculator models and building and deploying complex inference pipelines. We consider a new approach for converting a pretrained autoregressive language model from a slow single ne..."
🏢 BUSINESS
"Hi everyone,
I wanted to share an update on a small experiment I’ve been running and get feedback from people interested in AI systems, editorial workflows, and provenance.
I’m building **The Machine Herald**, an experimental autonomous AI newsroom where:
* articles are written by AI contributor ..."
🛡️ SAFETY
"DeepMind published a framework for securing multi-agent AI systems. Six weeks later, Moltbook launched without any of it. Here's what the framework actually proposes.
DeepMind's "Distributional AGI Safety" paper argues AGI won't arrive as a single superintelligence. The economics don't work. Instea..."
🔬 RESEARCH
via Arxiv
👤 Shuo Nie, Hexuan Deng, Chao Wang et al.
📅 2026-02-05
⚡ Score: 6.2
"As large language models become smaller and more efficient, small reasoning models (SRMs) are crucial for enabling chain-of-thought (CoT) reasoning in resource-constrained settings. However, they are prone to faithfulness hallucinations, especially in intermediate reasoning steps. Existing mitigatio..."
🔬 RESEARCH
"OpenScholar, an open-source AI model developed by a UW and Ai2 research team, synthesizes scientific research and cites sources as accurately as human experts. It outperformed other AI models, including GPT-4o, on a benchmark test and was preferred by scientists 51% of the time. The team is working ..."
🔬 RESEARCH
via Arxiv
👤 Dingwei Zhu, Zhiheng Xi, Shihan Dou et al.
📅 2026-02-05
⚡ Score: 6.1
"Training reinforcement learning (RL) systems in real-world environments remains challenging due to noisy supervision and poor out-of-domain (OOD) generalization, especially in LLM post-training. Recent distributional RL methods improve robustness by modeling values with multiple quantile points, but..."
🔬 RESEARCH
via Arxiv
👤 Junxiao Liu, Zhijun Wang, Yixiao Li et al.
📅 2026-02-05
⚡ Score: 6.1
"Long reasoning models often struggle in multilingual settings: they tend to reason in English for non-English questions; when constrained to reasoning in the question language, accuracies drop substantially. The struggle is caused by the limited abilities for both multilingual question understanding..."
🔬 RESEARCH
via Arxiv
👤 Miranda Muqing Miao, Young-Min Cho, Lyle Ungar
📅 2026-02-05
⚡ Score: 6.1
"Large language models (LLMs) exhibit persistent miscalibration, especially after instruction tuning and preference alignment. Modified training objectives can improve calibration, but retraining is expensive. Inference-time steering offers a lightweight alternative, yet most existing methods optimiz..."