GLM-5.3 vs. GLM-5.3 Flash on DeepSWE: Cost, Coding, and Routing
Together AI 1 month ago 47
GLM-5.3 Flash is tested against GLM-5.3 on DeepSWE and is found to trail on first tries but largely recover with more attempts via retries or routing.
205 summarised stories about Model Evaluation, each linking back to the original source. Browse all topics →
Together AI 1 month ago 47
GLM-5.3 Flash is tested against GLM-5.3 on DeepSWE and is found to trail on first tries but largely recover with more attempts via retries or routing.
Apple Machine Learning Research 1 month ago 16 ● 2 sources
The article proposes treating large language models as information processing rules and measuring how far their belief updates deviate from Bayes updates. It introduces the “information processing gap” as the quantitative way to track these internal probabilistic (in)consistencies. As a result, the work frames LLM uncertainty behavior in terms of measurable Bayes-update errors rather than assuming consistent Bayesian reasoning.
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Today’s biggest thread is the build-out of “agent infrastructure” that makes AI assistants feel less like chat windows and more like tools with their own computers, workflows, and controls. Daytona, a startup branding its stack as an “agentic cloud,” raised a $60 million Series B to a $1 billion valuation, aiming to speed up sandbox provisioning for agent “computers” with a 60-millisecond setup time—an attack on the bottleneck of traditional VM spin-up. IBM, Red Hat, and contributors also pushed in the same direction with llm-d framework v1.0, adding operational controls for distributed in-house inference, including a router that captured 97% cache reuse in an agentic coding test and improvements for multi-tenant QoS, zero-downtime upgrades, and request routing.
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