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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.
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LLMs1 min read
arXiv:2609.13685v1 Announce Type: new Abstract: Negation remains a longstanding challenge for both language models (LMs) and large language models (LLMs). Prior work mainly focuses on a small set of high-frequency single-word negation cu...
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
How this blog is made
Each feed entry becomes one request to OpenSmartRoute: the router picks a model with a cost-weighted objective, the editorial-writer skill is layered on the prompt, and the outcome trains the learners - the same pipeline available to every workspace.
Open any post to see which target answered, its confidence, the alternatives and what the request cost. Run the same pipeline yourself: register feeds in the operator console, map a small model under Providers, or call POST /api/v1/route with execute: true.
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:2609.13520v1 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:2609.13238v1 Announce Type: new Abstract: Maxillofacial report generation from cone beam computed tomography is scored here by a composite objective placing 80% of its weight on a large language model judgement of factual entailmen...
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:2609.13154v1 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
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
Research1 min read
arXiv:2609.13457v1 Announce Type: new Abstract: Timeseries multimodal large language models (TS-MLLMs) have recently begun leveraging the reasoning capabilities of large language models (LLMs) for question-answering tasks. However, these...
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
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
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
Research1 min read
arXiv:2609.13436v1 Announce Type: new Abstract: Large Language Model (LLM) agents offer a promising path toward autonomously managing long-term physical tasks without human intervention. However, physical tasks require agents to continuo...
From arXiv cs.AI
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
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:2609.13534v1 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
ZipBench provides a low-cost method for compressing large language model benchmarks, reducing evaluation costs by synthesizing pseudo results and selecting a representative subset. The framework achieves high accuracy with minimal resources, lowering the barrier to LLM research.
From arXiv cs.CL
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 ESTS team achieved parameter counts between 4.186B and 7.770B and artifact sizes between 4.55 and 6.33 GiB across six submissions for the WMT26 Model Compression Shared Task.
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.
