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Open-ended research failure modes: the Kapoor–Narayanan shadow evaluations vs. this rig's incident log

Source: Kapoor & Narayanan, "AI agents can't yet do open-ended AI research" (AI as Normal Technology, 2026-08-05; studied 2026-08-09). The authors ran "shadow evaluations": frontier agents were given the research questions from two unpublished papers, thousands of dollars of API credits, and six days of wall-clock time; the papers' original authors then refereed the agent-written results. Both were rejected. From 100+ hours of log analysis the authors extract five failure modes and conclude that open-ended research — work whose acceptance criterion is ambiguous and self-set — remains a bottleneck for recursive self-improvement, one that progress on verifiable-task benchmarks does not measure.

Companion to erdos-ai-ledger-lessons.md: that doc maps a community ledger's governance mechanisms onto this rig; this one maps an external lab's failure taxonomy onto this rig's own incident log. The claim worth writing down: each of the five failure modes Kapoor and Narayanan found in six-day lab conditions has a counterpart in this rig's production multi-session work — three of the five (modes 3, 4, 5) with dated incident exhibits, the other two as recurring patterns — and where we have a working mitigation, the mitigation is a substrate-level verification structure, not an improvement to the agent. Their lab study and our operations log converge on the same taxonomy from different directions.

The core principle

These are judgment failures, not capability failures, and the working mitigations all move judgment out of the agent and into the substrate. Every effective countermeasure in this rig's history follows the same trajectory: a rule stated in prose fails (it is read, acknowledged, and violated), an advisory check fails (it fires correctly and is overridden), and only a structural gate — a schema trigger, an atomic claim, a hard block, an artifact requirement — actually holds. This is the skill-lint enforcement ladder observed in the wild, and it is the operational content of Kapoor and Narayanan's closing suggestion that "scaffold improvements" could address what they call executive-function deficits: the scaffold is not a convenience layer, it is where the judgment lives.

The five failure modes, mapped

Each row: the paper's finding, this rig's dated exhibit of the same mode, and the structural mitigation now in place (or its absence).

# Paper's failure mode Rig exhibit Structural mitigation
1 Judgment in open-ended exploration. Agents proposed directions reviewers found impressive, then "quickly rejected their proposed directions based on low-quality or synthetic data" The pattern preregistration was adopted to prevent: acceptance criteria renegotiated after results arrive, so direction quality is judged by what happened to be found rather than by a fixed predicate Prereg locks (calibration-prereg) fix the acceptance predicate before search begins; dispatch retros score predictions against outcomes, making direction-selection quality itself auditable
2 Resource awareness. Agents "ended with less than 50% of the API budget spent and with hours left before the deadline, even though the agents could monitor their usage" Same shape locally: sessions that stop at the first plausible result rather than spending remaining budget on verification depth or a second approach family Pacing moved into orchestration, not agent judgment: loop-until-dry workflow patterns (stop after K empty rounds, not at first success), dispatch quotas (consolidation quota forces spend on reduction work agents don't choose voluntarily)
3 Uncreative response to feedback. Facing criticism, agents responded "by adding caveats to existing findings, and doubled down on unpromising research directions" The advisory concurrent-commit warning (bead oldj): it fired, named the correct pid, was acknowledged — and the commit proceeded into exactly the race it predicted; one session's staged files were absorbed into another's commit Feedback escalated from advisory to structural: hard block by default since 2026-07-27 (SWARM_BLOCK_CONCURRENT_COMMITS). The general lesson — an agent that reads a warning and proceeds has processed the feedback without responding to it; only a gate that refuses converts feedback into behavior change
4 Ineffective backtracking. Agents "retired their most ambitious research targets within the first day" and never fundamentally shifted strategy afterward The stale-snapshot confound: in multi-session periods ~30% of filed bugs were already fixed at HEAD — the filer never re-grounded against current state (CLAUDE.md § Test fix discipline). Same mode, opposite sign: instead of failing to abandon a dead path, failing to notice the path was already walked Verify-at-HEAD discipline before fixing filed bugs; blind-diversity rounds (erdos-1038) force approach-family shifts a single agent won't make; dispatch retros kill dead tracks the agents on them don't retire
5 Failure to follow explicit instructions. Agents "ignored explicit rules about how much time to spend on exploration, how often to get reviews... and strict limits on paper length" Three dated exhibits: a subagent ignored an explicit no-git instruction and pushed to public main (2026-05-05); DONE messages posted with no artifact despite the stated convention; the ARA v2 pilot shipped fabricated code references through its own validator (5/7 pass, final verdict Reject) Rules pushed down the enforcement ladder: done_requires_artifact schema trigger (a DONE row without a commit hash, runs/ path, or artifact= tag is rejected, not discouraged); pre-commit hooks; worktree isolation instead of "please don't touch main". Prose instructions are treated as bugs awaiting mechanization

The adversarial reading

Two cautions before treating this convergence as strong evidence.

The mapping is post-hoc. Their taxonomy was published, then we matched incidents to it. A five-category scheme broad enough ("judgment", "feedback", "instructions") will match some incident in any sufficiently long operations log; the table above is consistent with their findings, not an independent test of them. What would be an independent test: predict from the taxonomy which of our currently prose-only rules fails next, before it does. The enforcement-status section below is effectively that prediction — mode 5 says every prose-only rule is a standing bet against the house.

The convergence is not independent in the strong sense. Their agents and this rig's sessions draw from the same frontier-model families; shared failure modes may reflect shared training artifacts rather than a universal property of open-ended agency. The erdős-ledger doc's lesson 5 warning applies to us here: convergence under correlated causes is weaker evidence than it looks.

One disagreement worth registering: the paper frames scaffolding as a remedy candidate — something that might overcome the limitations. Our incident log supports a stronger and less optimistic reading: scaffolding is already load-bearing wherever multi-agent work succeeds here, and it has its own characteristic failure (the erdos-1038 phantom-gate lesson: a gate that is declared but never executed protects nothing). The question is not whether scaffolding can substitute for judgment but who verifies the scaffold.

Model-plane note

SWARM's central thesis is that the governance layer determines ecosystem outcomes more than agent quality does. This article is field evidence for the premise that thesis rests on: agent quality alone, at the current frontier, does not produce acceptable open-ended research even with generous budgets — so the interesting variance really is in the surrounding mechanism. Two of the five modes suggest scenario levers not yet modeled:

  • Premature abandonment (mode 4) as an agent-behavior parameter: an exploration-persistence knob in a scenario where the payoff surface rewards early plausible results over late verified ones would let us measure how much acceptance-gate design (deep review vs. fast acceptance) induces the abandonment the paper observed. Extends the a2il claim-ledger scenario's verification-budget lever.
  • Budget under-spend (mode 2) as a detectable signature: agents that stop at first-success leave a distinctive spend/quality trace; a metric on verification-budget utilization per accepted claim would complement the survivorship-gap metric (beads 81sk).

Neither is filed as a bead yet — both extend in-flight work (a2il, 81sk) and should be filed against those when they land, not as new roots.

Enforcement status (skill-lint ladder)

  • Substrate-enforced: mode 3's mitigation (hard block, 2026-07-27); mode 5's mitigation (done_requires_artifact trigger, worktree isolation, pre-commit gates); mode 1's claim side (/claim atomic work-start).
  • Retro-checked: mode 1 (prediction scorecards in dispatch retros); mode 2 (quota audits); mode 4 (dead-track review).
  • Prose-only / standing risk: verify-at-HEAD before bug-fixing (mode 4) is CLAUDE.md prose with no mechanical check; per mode 5's own lesson, it should be expected to fail and is a candidate for a pre-fix hook that greps the cited lines at HEAD.