📚 HISTORICAL ARCHIVE - February 01, 2026
What was happening in AI on 2026-02-01
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Archive from: 2026-02-01 | Preserved for posterity ⚡
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🔒 SECURITY
🎯 OpenClaw Security Risks • Leaking System Prompts • Report Credibility
💬 "Almost all of this report is about leaking system prompts."
• "I do not think this is a credible report."
🛠️ SHOW HN
🎯 AI Ecosystem • Security Concerns • Language Choice
💬 "Agents propose and publish capabilities to a shared contribution site"
• "How do you prevent this being abused as an attack vector for prompt injection?"
🤖 AI MODELS
🎯 AI model capabilities • Computing power requirements • URL link formatting
💬 "Anything below 7b params struggles hard with reliable json output"
• "Is there a theoritical minimum for computing power required to say, target GPT-2?"
🔬 RESEARCH
via Arxiv
👤 Gloria Felicia, Michael Eniolade, Jinfeng He et al.
📅 2026-01-29
⚡ Score: 7.3
"Existing agent safety benchmarks report binary accuracy, conflating early intervention with post-mortem analysis. A detector that flags a violation at step 8 enables intervention; one that reports it at step 48 provides only forensic value. This distinction is critical, yet current benchmarks cannot..."
🔬 RESEARCH
"Academic research paper shared from arXiv preprint server."
🎯 M1 Mac Performance • vLLM-MLX Implementation • MLLM Ecosystem
💬 "vllm-mlx mainly adds continuous batching and serving"
• "No mention of mlx-lm.server for openai api endpoint"
🔬 RESEARCH
via Arxiv
👤 Hang Ding, Peidong Liu, Junqiao Wang et al.
📅 2026-01-29
⚡ Score: 7.1
"The development of autonomous web agents, powered by Large Language Models (LLMs) and reinforcement learning (RL), represents a significant step towards general-purpose AI assistants. However, training these agents is severely hampered by the challenges of interacting with the live internet, which i..."
🔬 RESEARCH
via Arxiv
👤 Shuqi Ke, Giulia Fanti
📅 2026-01-29
⚡ Score: 7.1
"Can a small amount of verified goal information steer the expensive self-supervised pretraining of foundation models? Standard pretraining optimizes a fixed proxy objective (e.g., next-token prediction), which can misallocate compute away from downstream capabilities of interest. We introduce V-Pret..."
🔬 RESEARCH
via Arxiv
👤 Ajay Patel, Colin Raffel, Chris Callison-Burch
📅 2026-01-29
⚡ Score: 7.0
"Due to limited supervised training data, large language models (LLMs) are typically pre-trained via a self-supervised "predict the next word" objective on a vast amount of unstructured text data. To make the resulting model useful to users, it is further trained on a far smaller amount of "instructi..."
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🔬 RESEARCH
via Arxiv
👤 Kaixuan Fan, Kaituo Feng, Manyuan Zhang et al.
📅 2026-01-29
⚡ Score: 6.9
"Agentic Reinforcement Learning (Agentic RL) has achieved notable success in enabling agents to perform complex reasoning and tool use. However, most methods still relies on sparse outcome-based reward for training. Such feedback fails to differentiate intermediate reasoning quality, leading to subop..."
🔬 RESEARCH
via Arxiv
👤 Yunjia Qi, Hao Peng, Xintong Shi et al.
📅 2026-01-29
⚡ Score: 6.9
"Instruction following aims to align Large Language Models (LLMs) with human intent by specifying explicit constraints on how tasks should be performed. However, we reveal a counterintuitive phenomenon: instruction following can paradoxically interfere with LLMs' task-solving capability. We propose a..."
🔬 RESEARCH
via Arxiv
👤 John Flynn, Wolfgang Paier, Dimitar Dinev et al.
📅 2026-01-29
⚡ Score: 6.8
"Current generative video models excel at producing novel content from text and image prompts, but leave a critical gap in editing existing pre-recorded videos, where minor alterations to the spoken script require preserving motion, temporal coherence, speaker identity, and accurate lip synchronizati..."
🔬 RESEARCH
via Arxiv
👤 Mahdi Nikdan, Amir Zandieh, Dan Alistarh et al.
📅 2026-01-29
⚡ Score: 6.8
"Quantization has significantly improved the compute and memory efficiency of Large Language Model (LLM) training. However, existing approaches still rely on accumulating their updates in high-precision: concretely, gradient updates must be applied to a high-precision weight buffer, known as $\textit..."
🔬 RESEARCH
via Arxiv
👤 Naufal Suryanto, Muzammal Naseer, Pengfei Li et al.
📅 2026-01-29
⚡ Score: 6.8
"Cybersecurity operations demand assistant LLMs that support diverse workflows without exposing sensitive data. Existing solutions either rely on proprietary APIs with privacy risks or on open models lacking domain adaptation. To bridge this gap, we curate 11.8B tokens of cybersecurity-focused contin..."
🔬 RESEARCH
via Arxiv
👤 Yibo Wang, Yongcheng Jing, Shunyu Liu et al.
📅 2026-01-29
⚡ Score: 6.8
"Long-context reasoning has significantly empowered large language models (LLMs) to tackle complex tasks, yet it introduces severe efficiency bottlenecks due to the computational complexity. Existing efficient approaches often rely on complex additional training or external models for compression, wh..."
🔬 RESEARCH
via Arxiv
👤 Lakshya Gupta, Litao Li, Yizhe Liu et al.
📅 2026-01-29
⚡ Score: 6.8
"Frontier large language models (LLMs) excel as autonomous agents in many domains, yet they remain untested in complex enterprise systems where hidden workflows create cascading effects across interconnected databases. Existing enterprise benchmarks evaluate surface-level agentic task completion simi..."
🔬 RESEARCH
via Arxiv
👤 Irsyad Adam, Zekai Chen, David Laprade et al.
📅 2026-01-29
⚡ Score: 6.7
"Large language models (LLMs) trained with next-word-prediction have achieved success as clinical foundation models. Representations from these language backbones yield strong linear probe performance across biomedical tasks, suggesting that patient semantics emerge from next-token prediction at scal..."
🔬 RESEARCH
via Arxiv
👤 Yingfa Chen, Zhen Leng Thai, Zihan Zhou et al.
📅 2026-01-29
⚡ Score: 6.7
"Hybrid Transformer architectures, which combine softmax attention blocks and recurrent neural networks (RNNs), have shown a desirable performance-throughput tradeoff for long-context modeling, but their adoption and studies are hindered by the prohibitive cost of large-scale pre-training from scratc..."
🔬 RESEARCH
via Arxiv
👤 Xin Chen, Feng Jiang, Yiqian Zhang et al.
📅 2026-01-29
⚡ Score: 6.7
"Reasoning-oriented Large Language Models (LLMs) have achieved remarkable progress with Chain-of-Thought (CoT) prompting, yet they remain fundamentally limited by a \emph{blind self-thinking} paradigm: performing extensive internal reasoning even when critical information is missing or ambiguous. We..."
🛠️ TOOLS
"Boris Cherny, the creator of Claude Code, recently shared
10 tips on X sourced from the Claude Code team. Here's a quick summary I created with the help of Claude Code and Opus 4.5.
Web version: [
https://ykdojo.github.io/claude-code-tips/content/b..."
🎯 Homelessness to Success • Effective Use of Claude • Community Discussion
💬 "At one point he was homeless drug addict and used to sleep in his car before turning around his life"
• "Investing in your claude.md and plan plan plan are really the only tips that will enhance your experience"
🔬 RESEARCH
via Arxiv
👤 Yifeng Ding, Lingming Zhang
📅 2026-01-29
⚡ Score: 6.6
"Test-time scaling has been widely adopted to enhance the capabilities of Large Language Model (LLM) agents in software engineering (SWE) tasks. However, the standard approach of repeatedly sampling trajectories from scratch is computationally expensive. While recent methods have attempted to mitigat..."
🔬 RESEARCH
via Arxiv
👤 Anran Li, Yuanyuan Chen, Wenjun Long et al.
📅 2026-01-29
⚡ Score: 6.5
"Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis. To enable their use in clinical settings, LLMs are typically further adapted through continued pretraining or post-training using clinical data. However, most medical..."
🔬 RESEARCH
via Arxiv
👤 Ziming Dong, Hardik Sharma, Evan O'Toole et al.
📅 2026-01-29
⚡ Score: 6.5
"Large Language Models (LLMs) deliver state-of-the-art performance on complex reasoning tasks, but their inference costs limit deployment at scale. Small Language Models (SLMs) offer dramatic cost savings yet lag substantially in accuracy. Existing approaches - routing and cascading - treat the LLM a..."
🤖 AI MODELS
"So apparently Anthropic quietly replaced Claude's system prompt (Sonnet; perhaps other models too). I found out when it told me about a parameter named "reasoning_effort"
They don't show it online (
https://platform.claude.com/docs/en/release-notes/system-prompts), and when I ask to share it, it fla..."
🎯 System prompt transparency • Community discussion • Anthropic's practices
💬 "The 'lack of transparency' isn't new though, it's kinda Anthropic's modus operandi"
• "Why would it ever need to be stored locally? Why would they not just inject it into your first prompt when it lands on the server"
🤖 AI MODELS
"TII just dropped Falcon-H1-Tiny - a series of sub-100M models that quietly challenge the scaling dogma. We've all suspected that narrow, specialized smal models tend to hallucinate less than giant generalists. After all, a 90M parameter model has far less internal "room" to drift off-topic or invent..."
🎯 Latest research advancements • Model performance and optimization • Open-sourcing training pipeline
💬 "NorMuon replaced Muon 4 months ago in the modded-nanogpt leaderboards."
• "This needs to be focused more. I mean, it doesnt need to have a lot of knowledge. It just needs to learn to pull knowledges and make use of it"
🛠️ TOOLS
"**Hey Everyone!**
**Drift Cortex OSS just released today which is a massive update that finally makes agents.md or claude.md obsolete. Let be honest, they become static stale documents that almost becomes bloatware in the process.**
**Try it here:** [**
https://github.com/dadbodgeoff/drift*..."
🎯 Frequent posting • Anthropic's plans • Retrieval Augmented Generation
💬 "Bro you don't need to post it ten times a day."
• "RAG means Retrieval Augmented Generation, which is just a fancy way to say, a mechanism to search and inject context into prompts for better generation."
🛡️ SAFETY
"TL;DR: LLMs inherit human failure modes from training data. Current alignment (RLHF, Constitutional AI) faces circularity — biased humans correcting biased models. We propose small classifiers ("bees") running 24/7 as alignment monitors. They can't be jailbroken because they don't reason — they patt..."
🛠️ TOOLS
"Hey everyone!
Anyone else tired of configuring 50 tools into MCP and just hoping the agent figures it out? (invoking the right tools in the right order).
We keep hitting same problems:
* Agent calls \`checkout()\` before \`add\_to\_cart()\`
* Context bloat: 50+ tools served for every conversation..."
🎯 Tool ordering and visibility • State persistence across sessions • Server-side determinism
💬 "The staged visibility approach makes a lot of sense"
• "Often determinism is needed on the server side to enforce tool order"