LLMs8 min read
AI Code Volume Up But Productivity Stays Flat
A Harvard study shows AI tools increase code lines but fail to boost completed work due to review bottlenecks.
From InfoQ: AI, ML and data engineering
Loading
90 days of the enterprise, pro and team plans free, no card needed. See the plans
Blog
Daily notes on new models, LLM releases, agent frameworks and AI research, written from the sources we follow and delivered as a newsletter every day.
Get the morning and evening issues
Every new post of the day, in one email. Confirmation required.
LLMs8 min read
A Harvard study shows AI tools increase code lines but fail to boost completed work due to review bottlenecks.
From InfoQ: AI, ML and data engineering
AI12 min read
Researchers found Large Language Models frequently answer using search snippets without reading the actual web pages. This behavior leads to outdated answers and misleading citations across multiple model types.
From Unite.AI
Photo: Yan Krukau
Mirror Particle raises capital to create an AI engine that simulates changing human motivations. The company rejects large language models in favor of a foundation model trained on longitudinal data.
From TechCrunch AI
Agents9 min read
Amazon SageMaker AI now supports fine-tuning search agents using multi-turn reinforcement learning, improving retrieval quality and reliability with minimal setup.
From AWS machine learning blog
Research1 min read
Controlling the output of Large Language Models (LLMs) is a central challenge for their reliable deployment, yet a clear understanding of the involved trade-offs remains elusive. Current approaches to conditioning are often evaluated wit...
From Apple machine learning research
AI2 min read
Jev is TypeSafe AI’s first model that makes typed decisions with probabilities. It is faster and cheaper than some large language models for small judgments.
From MarkTechPost
Agents2 min read
Jev is a new decision model that is faster and cheaper than traditional large language models. LangGraph helps build reliable AI workflows.
From LangChain blog
Agents2 min read
Amazon EKS, Elastic Fabric Adapter, and DeepEP boost large-scale MoE reinforcement learning training by 40% throughput.
From AWS machine learning blog
Agents1 min read
EXL built an AI-powered Medical intelligent document processing (IDP) solution on AWS, combining IDP with domain-specific large language models on Amazon SageMaker and Amazon Bedrock to extract, summarize, and query medical records at en...
From AWS machine learning blog
LLMs1 min read
arXiv:2609.13152v1 Announce Type: new Abstract: Large language models (LLMs) perform strongly on static science benchmarks, yet their ability to reason about the physical world through active experimentation remains poorly understood. We...
From arXiv cs.CL
LLMs1 min read
arXiv:2609.13534v1 Announce Type: new Abstract: We identify \textbf{Harmfulness Propagation Dynamics (HPD)}: for harmful prompts, the projection of the last-token hidden state onto a learned harm direction rises monotonically with transf...
From arXiv cs.CL
LLMs1 min read
arXiv:2609.13481v1 Announce Type: new Abstract: Large language models have made abstractive summarization remarkably fluent, but generated summaries can hallucinate facts, posing serious risks in biomedical and clinical domains. We addre...
From arXiv cs.CL
LLMs1 min read
arXiv:2609.13737v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed, the generation of harmful content has become a critical safety concern. Existing safeguards operate at the input, output, or strea...
From arXiv cs.CL
LLMs1 min read
arXiv:2609.13556v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown remarkable proficiency on general-purpose tasks, yet their performance often degrades in highly-specialized technical domains. Moreover, little is kn...
From arXiv cs.CL
LLMs1 min read
arXiv:2609.13154v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have made prompts increasingly large and complex. Techniques such as chain-of-thought reasoning (Wei et al., 2022) and in-context learning (B...
From arXiv cs.CL
Research1 min read
arXiv:2609.13580v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities across a wide range of natural language processing tasks. However, conventional fine-tuning typically relies on centralize...
From arXiv cs.AI
Research1 min read
arXiv:2609.13559v1 Announce Type: new Abstract: Large Language Models (LLMs) with function-calling capabilities are becoming critical for modern agentic AI systems. Nevertheless, current deployments typically route inferences to powerful...
From arXiv cs.AI
Research1 min read
arXiv:2609.13470v1 Announce Type: new Abstract: Although large language models (LLMs) have demonstrated remarkable capabilities, their reliance on cloud-scale infrastructure poses fundamental challenges for deployment in agentic pipeline...
From arXiv cs.AI
LLMs1 min read
arXiv:2609.13734v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed in real-world high-stakes applications, effective governance has become essential. Existing safeguards largely follow two paradigms...
From arXiv cs.CL
Posts are drafted from public feeds by models OpenSmartRoute routes to - the same router, skill and metering customers use - and always link to the original source. Corrections: support.
Archive (100)
