49 articles mentioning Meta-Llama-3-8B-Instruct
Meta has been poaching talent from Thinking Machines Lab. But it's a two-way street.
Meta has commandeered a big chunk of Amazon's homegrown CPUs (not GPUs) for AI agentic workloads, signaling that a new kind of chip race has begun.
arXiv:2604.21284v1 Announce Type: new Abstract: MemPalace is an open-source AI memory system that applies the ancient method of loci (memory palace) spatial metaphor to organize long-term memory for large language models; launched in April 2026, it accumulated over 47,000 GitHub stars in its first t
arXiv:2604.21579v1 Announce Type: cross Abstract: LLM-based automated program repair (APR) techniques have shown promising results in reducing debugging costs. However, prior results can be affected by data leakage: large language models (LLMs) may memorize bug fixes when evaluation benchmarks overl
arXiv:2604.20857v1 Announce Type: cross Abstract: Recent advances in autonomous ``AI scientist'' systems have demonstrated the ability to automatically write scientific manuscripts and codes with execution. However, producing a publication-grade scientific diagram (e.g., teaser figure) is still a ma
arXiv:2604.20899v1 Announce Type: cross Abstract: Scalable synthesis remains the gate between MOF discovery and industrial deployment, as scale-up know-how is fragmented across disparate reports. We introduce ESU-MOF, a literature-mined dataset and a positive-unlabeled learning strategy that fine-tu
arXiv:2507.15753v2 Announce Type: replace-cross Abstract: Generative machine learning models have revolutionized material discovery by capturing complex structure-property relationships, yet extending these approaches to the inverse design of three-dimensional metamaterials remains limited by comput
arXiv:2604.20111v1 Announce Type: new Abstract: Sparse additive models have attracted much attention in high-dimensional data analysis due to their flexible representation and strong interpretability. However, most existing models are limited to single-level learning under the mean-squared error cri
arXiv:2604.19809v1 Announce Type: cross Abstract: We introduce MIRROR, a benchmark comprising eight experiments across four metacognitive levels that evaluates whether large language models can use self-knowledge to make better decisions. We evaluate 16 models from 8 labs across approximately 250,00
arXiv:2604.20148v1 Announce Type: cross Abstract: Can small language models achieve strong tool-use performance without complex adaptation mechanisms? This paper investigates this question through Meta-Tool, a controlled empirical study comparing hypernetwork-based LoRA adaptation against carefully
arXiv:2601.11505v2 Announce Type: replace Abstract: Progress in Type 1 Diabetes (T1D) algorithm development is limited by the fragmentation and lack of standardization across existing T1D management datasets. Current datasets differ substantially in structure and are time-consuming to access and pro
arXiv:2604.21263v1 Announce Type: new Abstract: \textbf{Background:} Regulatory frameworks for AI in healthcare, including the EU AI Act and FDA guidance on AI/ML-based medical devices, require clinical decision support to demonstrate not only accuracy but auditability. Existing formal languages for
Meta is planning to layoff around 10 percent of employees in May, according to a memo from the company's chief people officer, Janelle Gale, published by Bloomberg. That means approximately 8,000 people will see their jobs cut. Meta will also be closing around 6,000 open roles, according to Gale. Th
arXiv:2604.20454v1 Announce Type: new Abstract: Metaphors are powerful framing devices, yet their source domains alone do not fully explain the specific associations they evoke. We argue that the interplay between source domains and semantic frames determines how metaphors shape understanding of com
Meta employees' activity at work is now being used to train the company's AI agents. As reported by Reuters, Meta is installing a tool it calls Model Capability Initiative (MCI) on US-based employees' computers that runs in work-related apps and websites, recording mouse movements, clicks, keystroke
arXiv:2604.19072v1 Announce Type: cross Abstract: Semi-supervised learning with manifold regularization is a classical framework for jointly learning from both labeled and unlabeled data, where the key requirement is that the support of the unknown marginal distribution has the geometric structure o
arXiv:2604.18759v1 Announce Type: new Abstract: Class imbalance is a widespread challenge in NLP tasks, significantly hindering robust performance across diverse domains and applications. We introduce Hardness-Aware Meta-Resample (HAMR), a unified framework that adaptively addresses both class imbal
arXiv:2604.19383v1 Announce Type: cross Abstract: Metal-organic frameworks (MOFs) are a major target of machine-learning-based property prediction, yet most models assume that a single framework representation maps to a single property value. This assumption becomes problematic for experimental MOFs
arXiv:2604.15702v2 Announce Type: replace Abstract: We introduce a cross-domain behavioural assay of monitoring-control coupling in LLMs, grounded in the Nelson and Narens (1990) metacognitive framework and applying human psychometric methodology to LLM evaluation. The battery comprises 524 items ac
Meta says that it has a new internal tool that is converting mouse movements and button clicks into data that can train its AI models.
arXiv:2604.17399v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong reasoning capabilities, and as existing approaches for enhancing LLM reasoning continue to mature, increasing attention has shifted toward meta-reasoning as a promising direction for further improve
arXiv:2604.15343v1 Announce Type: cross Abstract: We report a detailed autoethnographic case study of a single-subject who deliberately constructed and operated a multi-modal prompt-engineering system (System A) designed to externalize cognitive self-regulation onto a large language model (LLM). Wit
arXiv:2601.23179v2 Announce Type: replace Abstract: Targeted adversarial attacks on closed-source multimodal large language models (MLLMs) have been increasingly explored under black-box transfer, yet prior methods are predominantly sample-specific and offer limited reusability across inputs. We ins
arXiv:2604.16009v1 Announce Type: new Abstract: Metacognition, the ability to monitor and regulate one's own reasoning, remains under-evaluated in AI benchmarking. We introduce MEDLEY-BENCH, a benchmark of behavioural metacognition that separates independent reasoning, private self-revision, and soc
arXiv:2604.17277v1 Announce Type: new Abstract: Physical neural networks offer a transformative route to edge intelligence, providing superior inference speed and energy efficiency compared to conventional digital architectures. However, realizing scalable, end-to-end, fully analog recurrent neural
arXiv:2510.07591v3 Announce Type: replace Abstract: We present a system that uses LLMs as a tool in the development of Constructed Languages -- ConLangs, which we call IASC (Interactive Agentic System for ConLangs). The system is modular in that it creates each of the components -- phonology, morpho
arXiv:2407.09514v3 Announce Type: replace-cross Abstract: Recently, metal-organic frameworks (MOFs) have demonstrated their potential as solid-state electrolytes in proton exchange membrane fuel cells. However, the number of MOFs reported to exhibit proton conductivity remains limited, and the mecha
arXiv:2604.12919v2 Announce Type: replace Abstract: Metonymy and metaphor often co-occur in natural language, yet computational work has studied them largely in isolation. We introduce a framework that transforms a literal sentence into three figurative variants: metonymic, metaphoric, and hybrid. U
arXiv:2507.11687v4 Announce Type: replace-cross Abstract: Large language models excel at code generation but struggle with code linting, particularly in generalizing to unseen or evolving best practices beyond those observed during training. We introduce MetaLint, a meta-learning framework that form
arXiv:2604.17425v1 Announce Type: new Abstract: Meta-optics promises compact, high-performance imaging and color routing. However, designing high-performance structures is a high-dimensional optimization problem: mapping a desired optical output back to a physical 3D structure requires solving compu
arXiv:2511.21613v2 Announce Type: replace Abstract: Incorporating metadata in Large Language Models (LLMs) pretraining has recently emerged as a promising approach to accelerate training. However prior work highlighted only one useful signal-URLs, leaving open the question of whether other forms of
arXiv:2511.03122v3 Announce Type: replace-cross Abstract: Designing materials with targeted properties remains challenging due to the vastness of chemical space and the scarcity of property-labeled data. While recent advances in generative models offer a promising way for inverse design, most approa
arXiv:2602.11182v2 Announce Type: replace Abstract: Existing memory systems enable Large Language Models (LLMs) to support long-horizon human-LLM interactions by persisting historical interactions beyond limited context windows. However, while recent approaches have succeeded in constructing effecti
arXiv:2604.16317v1 Announce Type: cross Abstract: Urban data support a wide range of applications across multiple disciplines. However, at the global scale, there is no unified platform for urban data discovery. As a result, researchers often have to manually search through websites or scientific li
arXiv:2604.16360v1 Announce Type: cross Abstract: This paper presents an analysis of five years (2021 - 2025) of conference discourse across six digital art conferences, aiming to trace thematic shifts associated with the rapid development of emerging technologies, namely artificial intelligence (AI
arXiv:2604.17569v1 Announce Type: new Abstract: Automated Essay Scoring (AES) faces significant challenges in cross-prompt settings, where models must generalize to unseen writing prompts. To address this limitation, we propose MAPLE, a meta-learning framework that leverages prototypical networks to
arXiv:2604.17707v1 Announce Type: new Abstract: Clinical personality assessment screens response validity before interpreting substantive scales. LLM evaluation does not. We apply the validity scaling framework from the PAI and MMPI-3 to metacognitive probe data from 20 frontier models across 524 it
arXiv:2604.17690v1 Announce Type: cross Abstract: Reconfigurable intelligent surfaces (RISs) modify signal reflections to enhance wireless communication capabilities. Classical RIS phase optimization is highly non convex and challenging in dynamic environments due to high interference and user mobil
arXiv:2604.13366v2 Announce Type: replace Abstract: Accurate modeling of robot dynamics is essential for model-based control, yet remains challenging under distributional shifts and real-time constraints. In this work, we formulate system identification as an in-context meta-learning problem and com
arXiv:2604.16957v1 Announce Type: new Abstract: We present Open-TQ-Metal, the first implementation of fused compressed-domain attention on Apple Silicon, enabling 128K-context inference for Llama 3.1 70B on a single 64GB consumer Mac -- a configuration impossible with all existing inference framewor
arXiv:2604.18399v1 Announce Type: new Abstract: Daily infrastructure management in preparation for disasters is critical for urban resilience. When bridges remain resilient against disaster-induced external forces, access to hospitals, shops, and residences via metapaths can be sustained, maintainin
arXiv:2601.18731v2 Announce Type: replace Abstract: Alignment of Large Language Models (LLMs) aims to align outputs with human preferences, and personalized alignment further adapts models to individual users. This relies on personalized reward models that capture user-specific preferences and autom
arXiv:2604.10126v2 Announce Type: replace-cross Abstract: Metamorphic testing (MT) is a widely recognized technique for alleviating the oracle problem in software testing. However, its adoption is hindered by the difficulty of constructing effective metamorphic relations (MRs), which often require d
arXiv:2505.11237v4 Announce Type: replace-cross Abstract: Metaphorical imagination, the ability to connect seemingly unrelated concepts, is fundamental to human cognition and communication. While understanding linguistic metaphors has advanced significantly, grasping multimodal metaphors, such as th
arXiv:2604.14562v1 Announce Type: new Abstract: Accurate thermal modeling in metal additive manufacturing (AM) is essential for understanding the process-structure-performance relationship. While prior studies have explored generalization across unseen process conditions, they often require extensiv
Starting April 19, the price of the Meta Quest 3S (128GB) and Meta Quest 3S (256GB) will go up by $50 to $349.99 and $449.99, respectively. The price of the Meta Quest 3 is going up by $100 to $599.99.
arXiv:2603.00137v2 Announce Type: replace-cross Abstract: Knowledge tracing (KT) models are commonly evaluated by training on early interactions from all students and testing on later responses. While effective for measuring average predictive performance, this evaluation design obscures a cold star
arXiv:2604.13713v1 Announce Type: new Abstract: Metaphor detection models achieve strong benchmark performance, yet it remains unclear whether this reflects transferable generalization or lexical memorization. To address this, we analyze generalization in metaphor detection through RoBERTa, the shar
arXiv:2604.11914v1 Announce Type: new Abstract: Self-monitoring capabilities -- metacognition, self-prediction, and subjective duration -- are often proposed as useful additions to reinforcement learning agents. But do they actually help? We investigate this question in a continuous-time multi-times