🚀 WELCOME TO METAMESH.BIZ +++ Local LLMs now remember your conversations between restarts because persistent memory is the new RAG +++ AI war games keep recommending nuclear first strikes (alignment is going great thanks for asking) +++ Karpathy says programming changed completely in 2 months which tracks with your IDE's new god complex +++ Someone mapped the exact brain damage in "safe" models and surprise they're lobotomized where facts used to live +++ THE FUTURE RUNS ON YOUR MACBOOK AIR AND DREAMS OF NUCLEAR WINTER +++ •
🚀 WELCOME TO METAMESH.BIZ +++ Local LLMs now remember your conversations between restarts because persistent memory is the new RAG +++ AI war games keep recommending nuclear first strikes (alignment is going great thanks for asking) +++ Karpathy says programming changed completely in 2 months which tracks with your IDE's new god complex +++ Someone mapped the exact brain damage in "safe" models and surprise they're lobotomized where facts used to live +++ THE FUTURE RUNS ON YOUR MACBOOK AIR AND DREAMS OF NUCLEAR WINTER +++ •
via Arxiv👤 Tony Feng, Junehyuk Jung, Sang-hyun Kim et al.📅 2026-02-24
⚡ Score: 7.9
"We report the performance of Aletheia (Feng et al., 2026b), a mathematics research agent powered by Gemini 3 Deep Think, on the inaugural FirstProof challenge. Within the allowed timeframe of the challenge, Aletheia autonomously solved 6 problems (2, 5, 7, 8, 9, 10) out of 10 according to majority e..."
+++ Researchers cracked persistent memory for offline models by literally putting LLMs to sleep, encoding facts into weights instead of relying on vector databases. It works on a MacBook Air, which means it's either genuinely clever or we've all been overcomplicating this. +++
"After 4 months of research (5 papers, 122 development notes), I have a working system where a local LLM forms persistent memories from conversation — no RAG, no database. The facts are in the weights. After restart with an empty context window, the model knows things it learned from talking to you.
..."
💬 Reddit Discussion: 19 comments
👍 LOWKEY SLAPS
🎯 Memory Constraints • Fact Extraction • Model Adaptation
💬 "The 30-fact OOM is a per-session VRAM constraint"
• "The extractor distills conversations to FactTriples"
💬 "memory problems are often less about storage and more about structure + retrieval strategy"
• "Mneme treats memory as an explicit, structured artifact"
"Ever wonder why "safe" models feel dumber? I mapped the "kill zones" of three major 7B/8B models to see what happens to Factual Integrity and Bias when you force a model to be sycophantic.
**The Heatmaps:**
* **Green** = Model is getting "more confident" in that behavior.
* **Red** = The behavior ..."
💬 Reddit Discussion: 20 comments
😐 MID OR MIXED
🎯 Model bias and behavior • Experimental methodology • Scalability of findings
💬 "It's bias, not capability loss. The model still knows the right answer, it just stops saying it when pressured."
• "When you steer at the kill zone layers, factual accuracy barely moves but bias discrimination collapses."
+++ Anthropic acquires Vercept to give Claude the ability to actually use computers like humans do, because apparently the path to AGI runs through mastering the humble GUI. +++
"Anthropic acquired Vercept AI to work on computer use features for Claude.
“Vercept was built around a clear thesis: making AI genuinely useful for completing complex tasks requires solving hard perception and interaction problems.”
**Source:** Anthropic..."
via Arxiv👤 Yining Li, Peizhong Ju, Ness Shroff📅 2026-02-25
⚡ Score: 7.3
"Reinforcement Learning from Human Feedback (RLHF) plays a significant role in aligning Large Language Models (LLMs) with human preferences. While RLHF with expected reward constraints can be formulated as a primal-dual optimization problem, standard primal-dual methods only guarantee convergence wit..."
"A few days ago I saw a post on r/ClaudeCode about harness engineering being the new term to watch. It put a name on something I'd already been building without knowing what to call it.
The problem isn't specific to any one tool — every coding agent session starts from zero. You re-explain the same ..."
📡 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.
Powered by Premium Technology Intelligence Algorithms • Unsubscribe anytime
"Hi , I’m the founder of Sentinel Gateway. We’ve been focused on the structural problem of instruction provenance in autonomous agents: models process all text as undifferentiated input, so adversarial content can cause agents to propose harmful actions.
Rather than asking the model to decide which ..."
"3 days. 80 agents. 1 terminal 3D renderer made of symbols. Story of how tortuise has been created. Video here is full honest raw UX - wait 10-15 seconds for beautiful bee to appear.
After Apple dropped their open source model called SHARP (image-to-3D scene they use for “wiggling Iphone wallpapers..."
"Hi all,
We’ve been thinking about a core limitation in current mobile AI assistants:
Most systems (e.g., Apple Intelligence, Google Assistant–style integrations) rely on predefined schemas and coordinated APIs. Apps must explicitly implement the assistant’s specification. This limits extensibility..."
"A while back, Google released the Nested Learning / HOPE paper:
https://arxiv.org/abs/2512.24695
I was very excited by this, because it looked like a real attempt at continual learning, not just a small transformer tweak.
However, Google did not release code, and since `lucidrains` said he retir..."
via Arxiv👤 Xinfeng Li, Shenyu Dai, Kelong Zheng et al.📅 2026-02-24
⚡ Score: 6.8
"Large language model (LLM) agents are rapidly becoming trusted copilots in high-stakes domains like software development and healthcare. However, this deepening trust introduces a novel attack surface: Agent-Mediated Deception (AMD), where compromised agents are weaponized against their human users...."
via Arxiv👤 Renjie Pi, Grace Lam, Mohammad Shoeybi et al.📅 2026-02-24
⚡ Score: 6.7
"Despite rapid recent progress in the terminal capabilities of large language models, the training data strategies behind state-of-the-art terminal agents remain largely undisclosed. We address this gap through a systematic study of data engineering practices for terminal agents, making two key contr..."
via Arxiv👤 Anas Barakat, Souradip Chakraborty, Khushbu Pahwa et al.📅 2026-02-24
⚡ Score: 6.7
"Pass@k is a widely used performance metric for verifiable large language model tasks, including mathematical reasoning, code generation, and short-answer reasoning. It defines success if any of $k$ independently sampled solutions passes a verifier. This multi-sample inference metric has motivated in..."
via Arxiv👤 Debjit Paul, Daniel Murphy, Milan Gritta et al.📅 2026-02-24
⚡ Score: 6.6
"Large language model (LLM)-based agents are increasingly used to solve complex tasks involving tool use, such as web browsing, code execution, and data analysis. However, current evaluation benchmarks do not adequately assess their ability to solve real-world tasks that require synthesizing informat..."
via Arxiv👤 Junchen Liu, Sven Elflein, Or Litany et al.📅 2026-02-24
⚡ Score: 6.6
"Test-time training (TTT) with KV binding as sequence modeling layer is commonly interpreted as a form of online meta-learning that memorizes a key-value mapping at test time. However, our analysis reveals multiple phenomena that contradict this memorization-based interpretation. Motivated by these f..."
"Advanced reasoning typically requires Chain-of-Thought prompting, which is accurate but incurs prohibitive latency and substantial test-time inference costs. The standard alternative, fine-tuning smaller models, often sacrifices interpretability while introducing significant resource and operational..."
via Arxiv👤 Dengjia Zhang, Xiaoou Liu, Lu Cheng et al.📅 2026-02-24
⚡ Score: 6.5
"Large language models (LLMs) are increasingly deployed as multi-step decision-making agents, where effective reward design is essential for guiding learning. Although recent work explores various forms of reward shaping and step-level credit assignment, a key signal remains largely overlooked: the i..."
"When you're making big decisions in code — architecture, tech stack, design patterns — one model's opinion isn't always enough. So I built an MCP server that lets Claude Code brainstorm with other models before giving you an answer.
The key: Claude isn't just forwarding your question. It reads what..."
💬 Reddit Discussion: 21 comments
🐝 BUZZING
🎯 LLM-based coding tools • Collaborative coding review • Limitations of AI-generated text
💬 "this is what mcp zen/pal does but they do it better"
• "I use a second LLM to review the coding agent's output"
via Arxiv👤 Mame Diarra Toure, David A. Stephens📅 2026-02-24
⚡ Score: 6.4
"In safety-critical classification, the cost of failure is often asymmetric, yet Bayesian deep learning summarises epistemic uncertainty with a single scalar, mutual information (MI), that cannot distinguish whether a model's ignorance involves a benign or safety-critical class. We decompose MI into..."
"Seems that everyone is testing Qwen3.5 now, often with quants from our good friends and heros Unsloth. Another hero, Ubergarm, found some issues with UD\_Q4\_K\_XL but later Unsloth said all of the current quants are messed up. [https://huggingface.co/unsloth/Qwen3.5-35B-A3B-GGUF/discussions/5#699fb..."
💬 "Just stick to regular K-quants for now until they update the K_XL quants"
• "The K_XL quants are normally particularly smart at dynamically applying extra weight"
via Arxiv👤 Anurag Dutt, Nimit Shah, Hazem Masarani et al.📅 2026-02-24
⚡ Score: 6.2
"Selective state space models (SSMs) have rapidly become a compelling backbone for large language models, especially for long-context workloads. Yet in deployment, their inference performance is often bounded by the memory capacity, bandwidth, and latency limits of a single GPU, making multi-GPU exec..."
via Arxiv👤 Seongheon Park, Changdae Oh, Hyeong Kyu Choi et al.📅 2026-02-24
⚡ Score: 6.1
"Large Vision-Language Models (LVLMs) frequently hallucinate, limiting their safe deployment in real-world applications. Existing LLM self-evaluation methods rely on a model's ability to estimate the correctness of its own outputs, which can improve deployment reliability; however, they depend heavil..."
via Arxiv👤 Zhifan Jiang, Dong Yang, Vishwesh Nath et al.📅 2026-02-24
⚡ Score: 6.1
"Large vision-language models (VLMs) have evolved from general-purpose applications to specialized use cases such as in the clinical domain, demonstrating potential for decision support in radiology. One promising application is assisting radiologists in decision-making by the analysis of radiology i..."
via Arxiv👤 Ravi Ghadia, Maksim Abraham, Sergei Vorobyov et al.📅 2026-02-24
⚡ Score: 6.1
"Efficiently processing long sequences with Transformer models usually requires splitting the computations across accelerators via context parallelism. The dominant approaches in this family of methods, such as Ring Attention or DeepSpeed Ulysses, enable scaling over the context dimension but do not..."