Tag
#evaluation
Reports tagged evaluation.
Reports
22 reportsOct 2026
10-17 prompt-optimization Can Tests Teach a Prompt to Improve? Automatic prompt optimization searches for instructions—and sometimes examples—that work better when executed. Its value depends on what the search changes, how it uses results, and what happens on cases kept outside the search. 10-12 multi-agent-systems How AI agents turn separate efforts into an accepted result See how useful AI teams connect responsibilities, evidence and acceptance—and judge their value by what one worker’s contribution lets another do and whether the resulting work survives the right checks. 10-09 finance How an AI Agent Turns a Market Hypothesis into a Tested Signal The agent can propose and implement a predictor. What makes it a tested signal is the experiment it survives—and the history of how that experiment was used. 10-04 context-compaction What an Agent Must Keep When It Compresses Its History A shorter history should leave the agent able to finish the task the user actually asked for. Sep 2026
09-28 decision-models What exists outside Jev—and how close has it come? From classical classifiers and entailment models to constrained LLMs and open decision scorers, the shared output shape hides very different methods and uneven evidence. 09-27 decision-models Is Jev a better decision model—or a better decision service? Jev's decision interface has a real operational niche, but fair alternatives and independent tests show why its model-level advantage remains an open question. 09-20 agent-harness How an AI Agent Should Decide Which Decisions Deserve Credit or Blame A final score does not explain a trajectory. Reliable improvement reconstructs the decision, tests fair alternatives, measures uncertainty, and applies the smallest reversible repair. 09-20 finance What a market view must contain before a financial agent builds a position A decision-ready market view is an accepted forecast packet: one resolvable claim, bound to the information available when it was made, passed through explicit stop rules, and handed to sizing without a hidden trade instruction. 09-19 agent-harness When an AI agent’s past becomes useful An agent learns from experience only when past outcomes change later choices through scoped, testable, revisable guidance that survives variation without hidden regressions or disproportionate cost. 09-13 agent-harness When an Agent Fails, Should You Change the Model or the System? The useful distinction is not weak intelligence versus bad engineering. It is which intervention removes the failures that matter—and whether the gain survives realistic costs and checks.