Large Language Model– tag –
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Multimodal LLM Agent ‘SynAgent’ Achieves Autonomous Synthesis of Highly Crystalline LiCoO2 Thin Films and Elucidates Substrate Temperature Control in Just 18 Experiments
arXiv USA Overview The 'SynAgent' framework, utilizing multimodal LLM agents, successfully achieved autonomous synthesis of highly crystalline LiCoO2 (001) thin films and elucidated the role of substrate temperature in crystallization wi... -
Scilight Press Introduces ‘Harness’ Framework Integrating LLM Agents and Materials Project to Enhance Perovskite Bandgap Prediction
Scilight Press Unknown Overview Scilight Press has proposed "Harness," a novel framework to integrate Large Language Model (LLM) agents with existing material databases in materials science. This framework translates LLM agent proposals ... -
LLM Stats Releases September 2026 Best Reasoning AI Model Rankings, Revealing Benchmark Data for 363 Models
LLM Stats Global Overview LLM Stats unveiled its latest rankings for AI models specializing in reasoning tasks on September 17, 2026, evaluating 363 models across 444 benchmarks. This ranking provides detailed comparisons of each model's... -
AI Workloads Drive Data Centers to Liquid Cooling, NVIDIA Optimizes Next-Gen Servers for Liquid Immersion
Connect CRE USA Overview The explosion of AI workloads is forcing data centers to adopt liquid cooling solutions as traditional air cooling can no longer manage the intense heat. Major data center providers like Equinix and Digital Realt... -
Open-Weight LLM GLM 5.3-flash Achieves High Scores on Cybersecurity Exploit Benchmarks, Industry Warned of One-Year Window to Fix Security
daily.dev (Tech Lead Digest) Unknown Overview The open-weight LLM, GLM 5.3-flash, recorded exceptionally high scores on cybersecurity exploit benchmarks like CyberGym and ExploitBench after its safety refusal capabilities were 'removed.'... -
New LLM Inference Algorithm for Compute-in-Flash Systems Achieves 15x KV Cache Traffic Reduction, Delivering Energy and Latency Savings for Llama-3.1-8B and Qwen-2.5-7B
arXiv Unknown Overview Research published on arXiv introduces a novel algorithm enabling LLM inference on Compute-in-Flash systems, mitigating memory bandwidth limitations. This method employs end-to-end integer-only quantization and a d... -
New ‘TAM’ Benchmark Exposes Critical Gaps in GPT-5’s Long-Horizon Procedural Reasoning, Achieving Only 1% Exact Match on ICD-10-CM Clinical Coding
arXiv Unknown Overview A new benchmark, Tasks over Application Manuals (TAM), has been introduced on arXiv to accurately assess LLMs' long-horizon procedural reasoning. Evaluating GPT-5, TAM revealed alarmingly low exact match performanc... -
LLM Test-Time Scaling: Candidate Generation Strategy Dramatically Increases Energy Consumption by 4.86x and Latency by 6.12x on A100 GPUs for Phi-3-mini and Qwen2.5-1.5B
Mobina Kashaniyan (referencing IEEE/ACM SC26 Workshop) Unknown Overview New research reveals that for LLM test-time scaling, the candidate generation strategy, not just the candidate count, critically impacts energy and performance. Eval... -
LLMAR: Tuning-Free Framework Boosts Recommendation Performance in Sparse, Text-Rich Industrial Domains via LLM Inference and Self-Verification
arXiv Unknown Overview arXiv introduces LLMAR, a tuning-free recommendation framework for sparse and text-rich industrial domains, demonstrating superior accuracy, explainability, and operational cost efficiency compared to traditional t... -
Samsung Research Unveils AnySimLite: Sub-700KB, Sub-30ms On-Device AI Matching 7B-Parameter LLMs for Speech Classification
Samsung Research South Korea Overview Samsung Research has introduced AnySimLite, a lightweight few-shot similarity encoder capable of delivering state-of-the-art performance for multiple on-device speech-adjacent classification tasks. O...
