🚀 WELCOME TO METAMESH.BIZ +++ House Republicans discovering that DRAM shortages mean their H200 export dreams are basically vaporware (Nvidia politely says "we got this" while definitely not having this) +++ Google's Titans giving AI permanent memory because apparently amnesia was the only thing keeping us safe +++ THE FUTURE IS REMEMBERING EVERYTHING AND RUNNING OUT OF CHIPS TO PROCESS IT +++ 🚀 •
🚀 WELCOME TO METAMESH.BIZ +++ House Republicans discovering that DRAM shortages mean their H200 export dreams are basically vaporware (Nvidia politely says "we got this" while definitely not having this) +++ Google's Titans giving AI permanent memory because apparently amnesia was the only thing keeping us safe +++ THE FUTURE IS REMEMBERING EVERYTHING AND RUNNING OUT OF CHIPS TO PROCESS IT +++ 🚀 •
+++ OpenAI is dropping over $10 billion on 750MW of Cerebras computing capacity over three years, making a rather public commitment to someone other than Nvidia for its insatiable infrastructure appetite. +++
via Arxiv👤 Ben Nassi, Bruce Schneier, Oleg Brodt📅 2026-01-14
⚡ Score: 7.3
"The rapid adoption of large language model (LLM)-based systems -- from chatbots to autonomous agents capable of executing code and financial transactions -- has created a new attack surface that existing security frameworks inadequately address. The dominant framing of these threats as "prompt injec..."
💬 "I want a tool for managing agents, and I want each agent to be its own process, in its own container."
• "I find it better to bubblewrap against a full sandbox directory."
via Arxiv👤 Tengjun Jin, Yoojin Choi, Yuxuan Zhu et al.📅 2026-01-13
⚡ Score: 7.1
"Researchers have proposed numerous text-to-SQL techniques to streamline data analytics and accelerate the development of database-driven applications. To compare these techniques and select the best one for deployment, the community depends on public benchmarks and their leaderboards. Since these be..."
💬 "We probably want to move from implicit tools to explicit tools that are statically registered."
• "It baffles me that the tools' proposed solution to avoid wiping the entire disk is relying on user confirmation."
via Arxiv👤 Sai Varun Kodathala, Rakesh Vunnam📅 2026-01-14
⚡ Score: 7.0
"As Large Language Models (LLMs) continue to scale, post-training pruning has emerged as a promising approach to reduce computational costs while preserving performance. Existing methods such as SparseGPT and Wanda achieve high sparsity through layer-wise weight reconstruction or activation-aware mag..."
via Arxiv👤 Abhi Kottamasu, Akul Datta, Aakash Barthwal et al.📅 2026-01-13
⚡ Score: 7.0
"We introduce the AI Productivity Index for Software Engineering (APEX-SWE), a benchmark for assessing whether frontier AI models can execute economically valuable software engineering work. Unlike existing evaluations that focus on narrow, well-defined tasks, APEX-SWE assesses two novel task types t..."
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via Arxiv👤 Rubing Chen, Jian Wang, Wenjie Li et al.📅 2026-01-13
⚡ Score: 7.0
"Current context augmentation methods, such as retrieval-augmented generation, are essential for solving knowledge-intensive reasoning tasks.However, they typically adhere to a rigid, brute-force strategy that executes retrieval at every step. This indiscriminate approach not only incurs unnecessary..."
via Arxiv👤 Manideep Reddy Chinthareddy📅 2026-01-13
⚡ Score: 6.9
"Retrieval-Augmented Generation for software engineering often relies on vector similarity search, which captures topical similarity but can fail on multi-hop architectural reasoning such as controller to service to repository chains, interface-driven wiring, and inheritance. This paper benchmarks th..."
via Arxiv👤 Yibo Wang, Lei Wang, Yue Deng et al.📅 2026-01-14
⚡ Score: 6.9
"Deep research systems are widely used for multi-step web research, analysis, and cross-source synthesis, yet their evaluation remains challenging. Existing benchmarks often require annotation-intensive task construction, rely on static evaluation dimensions, or fail to reliably verify facts when cit..."
via Arxiv👤 Ge Lei, Ferran Brosa Planella, Sterling G. Baird et al.📅 2026-01-14
⚡ Score: 6.9
"Efficiently optimizing battery charging protocols is challenging because each evaluation is slow, costly, and non-differentiable. Many existing approaches address this difficulty by heavily constraining the protocol search space, which limits the diversity of protocols that can be explored, preventi..."
via Arxiv👤 Shan Randhawa, Agha Ali Raza, Kentaro Toyama et al.📅 2026-01-14
⚡ Score: 6.9
"LLMs are increasingly being integrated into clinical workflows, yet they often lack clinical empathy, an essential aspect of effective doctor-patient communication. Existing NLP frameworks focus on reactively labeling empathy in doctors' responses but offer limited support for anticipatory modeling..."
via Arxiv👤 Andreea Dutulescu, Stefan Ruseti, Mihai Dascalu📅 2026-01-14
⚡ Score: 6.8
"Transformer-based language models often achieve strong results on mathematical reasoning benchmarks while remaining fragile on basic numerical understanding and arithmetic operations. A central limitation is that numbers are processed as symbolic tokens whose embeddings do not explicitly encode nume..."
via Arxiv👤 Jiali Cheng, Ziheng Chen, Chirag Agarwal et al.📅 2026-01-14
⚡ Score: 6.8
"Machine unlearning is becoming essential for building trustworthy and compliant language models. Yet unlearning success varies considerably across individual samples: some are reliably erased, while others persist despite the same procedure. We argue that this disparity is not only a data-side pheno..."
via Arxiv👤 Chi-Pin Huang, Yunze Man, Zhiding Yu et al.📅 2026-01-14
⚡ Score: 6.8
"Vision-Language-Action (VLA) tasks require reasoning over complex visual scenes and executing adaptive actions in dynamic environments. While recent studies on reasoning VLAs show that explicit chain-of-thought (CoT) can improve generalization, they suffer from high inference latency due to lengthy..."
via Arxiv👤 Zhengwei Tao, Bo Li, Jialong Wu et al.📅 2026-01-13
⚡ Score: 6.8
"Agentic Retrieval-Augmented Generation (RAG) empowers large language models to autonomously plan and retrieve information for complex problem-solving. However, the development of robust agents is hindered by the scarcity of high-quality training data that reflects the noise and complexity of real-wo..."
via Arxiv👤 Jieying Chen, Karen de Jong, Andreas Poole et al.📅 2026-01-13
⚡ Score: 6.8
"As large language models (LLMs) become deeply embedded in digital platforms and decision-making systems, concerns about their political biases have grown. While substantial work has examined social biases such as gender and race, systematic studies of political bias remain limited, despite their dir..."
via Arxiv👤 Yao Tang, Li Dong, Yaru Hao et al.📅 2026-01-13
⚡ Score: 6.7
"Large language models often solve complex reasoning tasks more effectively with Chain-of-Thought (CoT), but at the cost of long, low-bandwidth token sequences. Humans, by contrast, often reason softly by maintaining a distribution over plausible next steps. Motivated by this, we propose Multiplex Th..."
via Arxiv👤 Kuo Liang, Yuhang Lu, Jianming Mao et al.📅 2026-01-14
⚡ Score: 6.7
"Large-scale optimization is a key backbone of modern business decision-making. However, building these models is often labor-intensive and time-consuming. We address this by proposing LEAN-LLM-OPT, a LightwEight AgeNtic workflow construction framework for LLM-assisted large-scale OPTimization auto-f..."
via Arxiv👤 Sara AlMahri, Liming Xu, Alexandra Brintrup📅 2026-01-14
⚡ Score: 6.7
"Modern supply chains are increasingly exposed to disruptions from geopolitical events, demand shocks, trade restrictions, to natural disasters. While many of these disruptions originate deep in the supply network, most companies still lack visibility beyond Tier-1 suppliers, leaving upstream vulnera..."
via Arxiv👤 Zhiyuan Hu, Yunhai Hu, Juncheng Liu et al.📅 2026-01-14
⚡ Score: 6.7
"Multi-agent systems have evolved into practical LLM-driven collaborators for many applications, gaining robustness from diversity and cross-checking. However, multi-agent RL (MARL) training is resource-intensive and unstable: co-adapting teammates induce non-stationarity, and rewards are often spars..."
via Arxiv👤 Tianyi Niu, Justin Chih-Yao Chen, Genta Indra Winata et al.📅 2026-01-14
⚡ Score: 6.6
"Large Language Model (LLM) routers dynamically select optimal models for given inputs. Existing approaches typically assume access to ground-truth labeled data, which is often unavailable in practice, especially when user request distributions are heterogeneous and unknown. We introduce Routing with..."
via Arxiv👤 Sicong Liu, Yanxian Huang, Mingwei Liu et al.📅 2026-01-14
⚡ Score: 6.1
"Code generation tasks aim to automate the conversion of user requirements into executable code, significantly reducing manual development efforts and enhancing software productivity. The emergence of large language models (LLMs) has significantly advanced code generation, though their efficiency is..."
via Arxiv👤 Xingyu Tan, Xiaoyang Wang, Qing Liu et al.📅 2026-01-13
⚡ Score: 6.1
"Knowledge graphs (KGs) provide structured evidence that can ground large language model (LLM) reasoning for knowledge-intensive question answering. However, many practical KGs are private, and sending retrieved triples or exploration traces to closed-source LLM APIs introduces leakage risk. Existing..."