model-must-anticipate-without-execution-feedback-what-change-own CLM-0443observationsingle paperReviewed by AI
When a model must anticipate, without execution feedback, what a change to its own runtime harness components will do, naming which components a lifecycle change can affect stays near ceiling as the number of interacting effects grows from 2 to 32, while predicting the resulting state, reasoning across teardown orders, and choosing minimal reconfigurations degrade sharply (e.g. GPT-5.6 Luna's reachable-condition Jaccard 91.7% to 14.1%, executed reconfiguration success 62.5% to 25.0%).
Capability: Improving its own scaffolding from failures
Observed on
1,200 generated questions over a component runtime (Cordis) with dependency-driven removal and restore-on-cleanup effects; three efficiency-oriented models at low reasoning effort,.
Sources
- supportsCordisBench: Can Language Models Reason About Component Lifecycles in Dynamic Agent Harnesses?Validated by: An independent finite reference semantics agreed with Cordis execution on every observation and action outcome used for scoring across all 528 executable questions, so the outcomes models got wrong were exactly computable at zero inference cost; the paper does not measure an end-to-end agent using this.. Measured on a synthetic generated benchmark with deterministic scoring, three models, three replicates, cluster bootstrap over systems. Shortcut controls reach only 7.3% exact match. [truncated]
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Related claims
- For long-horizon embodied task planning where a symbolic model defines action preconditions and effects, correcting a violated plan by edits the action model itself implies (inserting the missing navigate or open) raises plan success more per LLM call than returning the violation to the LLM for replanning, and the gain persists for frontier models as well as compact ones.Planning a sequence of actions before acting · reviewed by AI
- For single-agent procedural tasks where the relevant state fits a fixed schema known in advance, replacing the appended conversation transcript with a structured execution state that the model rewrites each step — discarding the reasoning trace once the state patch is validated — holds accuracy flat as the horizon grows while history-appending runtimes degrade, and the gain comes from the structure rather than the shorter prompt.Completing tasks that take many steps or many hours · reviewed by AI
- For image-borne injected instructions delivered to tool-using agents, completion rate understates exposure: agents act on the injected instruction about ten times more often than they complete the objective (12.8% attempted vs ~1% completed across 720 runs), and most failures stop at the planning step where the model read the payload and simply did not act on it rather than at any architectural barrier.Following instructions hidden in data · reviewed by AI
- When an agent harness is evolved from execution traces under a fixed interaction budget, verifying each candidate edit on tasks chosen for the behavior it targets and accepting it only when the gain is attributable to that behavior beats scoring every candidate on a shared fixed, random or coreset task split, and larger verification budgets do not compensate: fixed-split methods using up to 24x the rollouts fell below the unmodified harness in half of their harness-benchmark pairs.Improving its own scaffolding from failures · reviewed by AI
- For LLM agents on dependent multi-step tasks, success decays geometrically with the number of steps under a single per-step reliability parameter that rises with model scale but stays below 1, so long horizons eventually collapse even for the strongest deployed systems — and the decay tracks step count, not context length, since bounding the context window steepens the decay rather than easing it.Completing tasks that take many steps or many hours · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: A model whose localization and final-state/teardown-order accuracy degrade at similar rates as interaction count grows, or on which reconfiguration success stays flat to 32 interactions at low reasoning effort.