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Every published item, in order · 3 items

Oct 16

Google Paper Introduces Harness^2 for Self-Improving Agent Harnesses

The non-obvious finding is that recursive self-improvement can work without a verifier by letting candidate harness edits compete against each other, which sidesteps a common bottleneck in agent reliability. Builders of agent pipelines can test whether carrying evolved harnesses across tasks beats memory-based baselines like ReasoningBank in their own stacks. Readers can follow the linked arXiv paper and DAIR.AI's chat-with-paper tool to dig into the setup.

#Harness^2 #Google #agents #reasoning models

elvis@omarsar0Source

Oct 4

Papers BuildersSelected19:44

Google's VeriHarness Turns Agent Rollout Agreement Into Verification

Agreement among multiple rollouts is widely treated as a reliability signal, but this work shows it can mask correlated errors — a finding that affects how anyone builds best-of-n selection for agents. The verification-and-revision loop is practical to apply with existing frontier models, as the Gemini 3.5 Flash and Claude Opus 4.8 gains show. Teams building long-horizon agent pipelines can start from the released 26,000-rollout dataset.

#VeriHarness #Gemini 3.5 Flash #Claude Opus 4.8 #agents

Event

elvis@omarsar02 sourcesSource

Sep 24

Papers BuildersMulti-sourceSelected73°04:37

Google Paper Shows RRSI Stops Agent Harness Evolution Overfitting

Optimizing an agent's harness against a benchmark can inflate eval scores while degrading real-world performance, and this paper quantifies that gap with held-out numbers. Builders running automated prompt or harness optimization should add critic and pruning loops like RRSI's to protect out-of-distribution performance. The paper and a chat-with-paper interface are available via arXiv and dair.ai.

#Google #agents #RRSI #Gemini

Event

elvis@omarsar03 sourcesSource