LLMs1 min read
Together AI offers a five-stage guide for moving to open models
Together AI released a playbook to help companies migrate from closed to open source models.
From Together AI blog
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LLMs1 min read
Together AI released a playbook to help companies migrate from closed to open source models.
From Together AI blog
AI2 min read
This guide explains what government policy is, how it influences various sectors, and the key elements involved in policy development and implementation.
From growth-engine
How this blog is made
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From Latent Space
LLMs1 min read
arXiv:2609.13520v1 Announce Type: new Abstract: While Large Language Models have improved rapidly, many fundamental questions remain about how to evaluate the knowledge and reasoning abilities they acquire, and how such evaluations relat...
From arXiv cs.CL
LLMs1 min read
arXiv:2609.13611v1 Announce Type: new Abstract: The WMT26 General MT task evaluates systems on 10 language pairs that have no human references (neither translated from scratch nor post-edited from MT output by humans). We describe how we...
From arXiv cs.CL
LLMs1 min read
arXiv:2609.13454v1 Announce Type: new Abstract: Clinical decisions are prospective, but clinical language models are often evaluated on retrospective records that reveal the final diagnosis, treatment response, and outcome. Such evaluati...
From arXiv cs.CL
LLMs1 min read
arXiv:2609.13768v1 Announce Type: new Abstract: Multi-hop question answering often fails when retrieval treats evidence as isolated matches to the original question, since the facts needed to answer a complex question are usually connect...
From arXiv cs.CL
LLMs1 min read
arXiv:2609.13389v1 Announce Type: new Abstract: Mapping textual specifications into formal representations is essential for ensuring the correctness of protocol designs and implementations. LLM-generated mappings, used for networking sec...
From arXiv cs.CL
Research1 min read
arXiv:2609.13422v1 Announce Type: new Abstract: LLM judges are increasingly used to evaluate and improve AI-generated outputs, yet their reliability for complex professional work remains unclear. We study this problem through Vibe Patent...
From arXiv cs.AI
Research1 min read
arXiv:2609.13637v1 Announce Type: new Abstract: Persistent agents need evaluations that distinguish identity facts they can recall from those they express and enact. We introduce PAI-Bench, a provider-neutral benchmark for fidelity to a ...
From arXiv cs.AI
Research1 min read
arXiv:2609.13475v1 Announce Type: new Abstract: Existing pipelines for clinical timeline extraction from case reports are evaluated using an expert reference and are limited by imperfect reference annotations and imprecise event alignmen...
From arXiv cs.AI
Agents2 min read
This post outlines a framework for customizing generative AI models on AWS, ranging from simple prompt engineering to training custom models. The spectrum guides engineers through the appropriate level of investment based on workload requirements and data needs.
From AWS machine learning blog
LLMs1 min read
The RIPPLE system improves workflow synthesis agents by isolating policy edits and evaluating their downstream effects through replay. Evaluations on Flow-HO show up to 23.1% improvement in validation success and maintain edit efficiency.
From arXiv cs.CL
Research1 min read
The BlueLM-GUI system, a 35B model, achieves strong performance on mobile GUI tasks through a real-device training approach. This flywheel system utilizes a multi-stage process of data salvage, real-device rollout, and evolving benchmarks to improve agent capabilities and transfer directly to production environments.
From arXiv cs.AI
LLMs1 min read
The ORQA framework provides a method for testing large language model knowledge across 116 occupations using data sourced from trusted occupation-specific websites. Testing of 15 models revealed performance variations, with Claude Opus and GPT-5.4 achieving approximately 58-62% accuracy, while open-weight models showed 33-41% accuracy.
From arXiv cs.CL
LLMs1 min read
The EAR approach partitions source corpora for retrieval-augmented generation using entity-aware windows. Experiments with Mistral, Gemma, and DeepSeek show a reduction in retrieved words of 37.5-40.2% and associated accuracy changes, primarily focusing on a cleaned MMLU subset.
From arXiv cs.CL
LLMs1 min read
CueMem reconstructs query-relevant dialogue context from retrieval cues, improving long-term conversational memory for LLMs. Experiments on LoCoMo and LongMemEval demonstrate improved performance and reduced input tokens compared to full-history approaches.
From arXiv cs.CL
LLMs1 min read
Researchers introduced ASCIL, a post-ASR correction framework that learns from misclassifications to reduce false wake-up activations in conversational AI. Evaluations on a proprietary dataset showed a significant relative error reduction, alongside improvements in intentional acceptance rates.
From arXiv cs.CL
Research1 min read
T-GADE, a system using thermodynamic genetic algorithms and LLMs, evolved structured artifacts like code paired with descriptions. Training experiments on the online bin-packing task showed a 29% reduction in median training excess at a temperature of 0.003, validating the approach’s effectiveness.
From arXiv cs.AI
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