interrupt-agent-step-level-critic-flags-critical-risk TEC-0135 · proposedInterrupt the agent when a step-level critic flags critical
A lightweight generative reward model, trained on causal-chain-backtracked step labels from failed trajectories plus injected errors in successful ones, asynchronously diagnoses the agent's last completed step and forces regeneration of the
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
A lightweight generative reward model, trained on causal-chain-backtracked step labels from failed trajectories plus injected errors in successful ones, asynchronously diagnoses the agent's last completed step and forces regeneration of the Filed by the drafting stage from FLARE: A Full-Lifecycle Dense Supervision Paradigm for Long-Horizon Coding Agents via Generative Reward Model (2609.23808) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Fail-to-pass Pass@N on initially failed tasks and pass-to-pass Avg@N on initially successful tasks, against global resampling, blind breakpoint rollout, and post-hoc RADAR-guided replay at matched branch budgets, with agent output tokens and turns reported; also ROC-AUC of the derived trajectory score and downstream SFT/RL pass rates.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.
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Sources
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