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Top signals · Oct 22–25, 2026 · Updated Oct 25, 16:04 UTC
A 32K-window proxy that beat full context on long agent histories
Show HN: The fastest, most token-efficient iOS Simulator MCP for AI agents
SCOPE: Compiling Natural-Language Policies into Neurosymbolic Predicate Programs
Policy enforcement for LLM agents has shifted from single-turn content moderation to compound, application-specific policies evaluated over multi-turn transcripts and tool-call trajectories. Fine-tuned guardrail model... Context & related coverage →
Post-Audit Mirage: Identifiability Limits of Offline Agent Verification
Offline tests can fail to reveal whether an agent update will remain safe after deployment. We operationalize this post-audit mirage using matched safe and harmful lifecycles that expose identical verifier-visible evi... Context & related coverage →
The Value of Memory in Language Agents
Memory enables language agents to carry useful distinctions from past experience into future decisions. Its value depends on which distinctions survive, whether a bounded agent can use them, and what can be recovered... Context & related coverage →
Simulated Control Evals
Control evaluations measure whether safeguards can prevent an AI model from causing undesired outcomes even when it is intentionally trying to do so. These evaluations typically use purpose-built control settings and... Context & related coverage →
Cost-Aware Abstention for LLM-Based IT Ticket Classification
Wrong automatic routing of an IT support ticket can cost more than human triage. We evaluate cost-aware abstention as an offline, cost-conditional readiness assessment: select a routing policy on calibration data unde... Context & related coverage →
Introducing our Training API
Spindle is an open-source API for training and fine-tuning open models — deploy a server with a single command and run any Tinker-compatible training code against it. Context & related coverage →
Expanding Claude availability and deployment choice on Google Cloud
Every organization has unique requirements for building and scaling generative AI. At Google Cloud, we are committed to giving developers model choice and architectural flexibility to run workloads however they need.... Context & related coverage →