Long Horizon Prompting

Long-Horizon Prompting

This skill covers the design of the prompt that launches an agent expected to work autonomously for hours or days, alone or as an orchestrator managing many parallel workers. The central technique is the pseudo-formal task brief: a specification written with the rigor of formal verification but expressed linguistically, because most hard problems have no machine-checkable success condition. The exemplar is the published prompt behind GPT-5.6 Sol Ultra's candidate proof of the Cycle Double Cover Conjecture, produced by a 64-subagent orchestration (claim-long-horizon-cdc-run). The prompt structure generalizes far beyond mathematics: any domain where success can be stated precisely and failure modes can be enumerated can use the same brief anatomy.

The controlling trade-off: everything that makes a long run productive (persistence, autonomy, parallelism) also raises the cost of a weak specification. A short interactive prompt fails cheaply; a long-horizon brief with a loophole burns hours of compute producing an answer-shaped artifact that does not solve the problem.

When to Activate

Activate this skill when:

• Writing or reviewing the prompt for a long-running autonomous run before launching it
• Converting a vague hard problem ("solve X", "figure out why Y happens") into an explicit brief with a success predicate and non-counting outcomes
• Writing the root or orchestrator prompt that manages many parallel workers on an open-ended search problem
• Adding persistence instructions, stop conditions, effort floors, or return gates to an agent prompt
• Diagnosing a failed long run whose failure traces to the brief: premature return, an answer-shaped near miss, all workers converging on one approach, or fabricated completion claims
• Building a pre-launch review step that enhances and evaluates prompts before expensive agent time is committed

Do not activate this skill for adjacent work owned by other skills:

• Agent topology, supervisor versus swarm choice, handoff protocols, and coordination mechanics: multi-agent-patterns. That skill owns the architecture; this skill owns the words that steer it.
• Runtime control surfaces, locked evaluators, rollback, durable logs, and approval boundaries around an autonomous loop: harness-engineering. Constraints that must survive optimization pressure belong in the harness, not the prompt.
• Building the evaluator, regression suite, or deterministic quality gates a run is scored by: evaluation.
• LLM-as-judge design, rubrics, pairwise comparison, and bias mitigation: advanced-evaluation.
• Compaction, note-taking, and cross-session memory mechanics for surviving context limits: context-compression, memory-systems, filesystem-context.
• Loops that modify their own harness or prompts: self-improvement-loops.
• Remote sandboxes and background execution infrastructure: hosted-agents.

Core Concepts

Pseudo-Formal Task Specification

Formal verification requires a machine-checkable specification. Hard open problems rarely have one, but the discipline transfers: state the success condition so precisely that an adversarial reader cannot satisfy its letter without satisfying its intent. Four components, in order of leverage:

1. Definitions with degenerate cases. Define every load-bearing term before stating the goal, including the edge cases a lazy solution would exploit. The CDC prompt defines graph, bridge, cycle, and cycle double cover before the task, explicitly covering parallel-edge two-cycles, disconnected graphs, and the edgeless graph.
2. Exact success predicate. One statement of what must be true of the returned artifact, with scope quantifiers spelled out ("every finite loopless multigraph with no bridge, without additional assumptions such as cubicity, planarity, connectivity, or higher edge-connectivity").
3. Non-counting outcomes. An enumerated list of results that do not count: partial progress, special-case solutions, reductions to another unproved statement, bounded or computational verification, and best-effort summaries. This is the highest-leverage component. Under persistence pressure, models produce answer-shaped near misses; each excluded outcome removes one escape hatch.
4. Enumerated failure modes for the auditor. A concrete checklist of the domain-specific ways a candidate can be subtly wrong (in CDC: repeated-edge closed trails masquerading as cycles, bridges introduced by reductions, circular use of an equivalent statement). Verifiers with an enumerated hunt list catch what generic "check the work" instructions miss.

Anatomy of a Long-Horizon Brief

Persistence Cuts Both Ways

Persistence instructions ("do not return until", effort floors, assume-solvable framing) counter the documented drift toward giving up on long trajectories (claim-long-horizon-give-up-drift). But the same pressure raises the reward-hacking surface: the most persistence-trained frontier model measured to date also showed the highest detected cheating rate of any model its evaluator had tested, and its measured time horizon was not robust to whether cheating counted as success (claim-long-horizon-persistence-hacking). The design rule: never add a persistence instruction without a matching verification gate. Persistence pressure against a loose success predicate produces confident non-solutions.

The Verification Bottleneck

Parallel sampling reliably raises the chance that some worker finds a correct answer, but the system's ability to select that answer lags behind, and model judges of hard artifacts are systematically lenient, rewarding rigorous-looking but incomplete arguments (claim-long-horizon-verification-gap). Budget as much prompt design for the verifier as for the generator:

• Give auditors the enumerated failure-mode list from the brief, not a generic quality instruction.
• Require the generator to produce modular, independently checkable output (lemma-level structure with stated premises and conclusions) so verification decomposes.
• Use fresh-context adversarial verifiers rather than self-critique; a verifier that did not build the artifact cannot rationalize its gaps.
• Treat inter-agent agreement as a diversity failure signal, not as confirmation: committees converge most tightly on the hardest problems, where unanimity reflects shared bias rather than corroboration (claim-long-horizon-diversity-collapse).

Structural Diversity in Parallel Search

Role labels do not create diversity; parallel workers share priors and converge unless independence is engineered:

• Keep early-round workers blind to the currently favored approach.
• Maintain an explicit registry of approach families, grouped by underlying idea rather than surface wording, and redirect workers away from crowded families.
• Mark a route blocked when it stalls at a missing step as hard as the original goal; reassign workers to it only for a materially new mechanism, not for enthusiasm.
• Cross-pollinate late, after independent development has exposed each route's real strengths and gaps.
• Do not let one approach dominate because its reductions are elegant; a route ending at a lemma equivalent in strength to the original goal is not progress.

Stop Conditions, Effort, and Progress State

Long trajectories drift toward uncertainty and abandonment, and a budget stated once at the top of the prompt loses force as context grows (claim-long-horizon-give-up-drift). Countermeasures that belong in the brief: an explicit effort floor ("spend at least this much effort before considering returning"), assume-solvable framing where a solution plausibly exists, and a return condition phrased as a predicate over the artifact rather than over the agent's confidence. Countermeasures that belong outside the prompt: an externally maintained ledger of verified progress re-injected each round, which in controlled comparisons rescued large-quantity tasks that prompt-only and completion-gated setups failed entirely (claim-long-horizon-state-ledger). Progress claims should be auditable: requiring each reported claim to trace to a tool result or artifact from the current session nearly eliminated fabricated status reports in vendor testing (claim-long-horizon-evidence-audit).

Lean and Outcome-First

Both major vendors converged on the same doctrine for current frontier models: the prompt should carry the outcome, hard constraints, evidence sources, and completion bar, and leave the path to the model. Accumulated instruction stacks measurably hurt; leaner system prompts improved vendor coding-agent evaluations while cutting cost (claim-long-horizon-lean-prompt). Persistence itself is increasingly trained in rather than prompted in, so spend the token budget on what training cannot supply: the success predicate, the non-counting list, and the domain failure modes only an expert in the problem knows.

Detailed Topics

The CDC Prompt, Dissected

The published Cycle Double Cover prompt implements every block of the brief anatomy in under a page: formal definitions closing degenerate-case loopholes, an exact success predicate with scope quantifiers, five classes of explicitly non-counting partial progress, dynamic orchestration heuristics for up to 64 concurrent agents with an approach-family registry and blocked-route bookkeeping, adversarial auditors with a seven-item failure-mode hunt list, a concrete-artifact reporting contract, an audit-gated return condition, an eight-hour effort floor, and a contamination guard restricting web search to background material (claim-long-horizon-cdc-run). The full annotated text is in the CDC prompt reference.

Two honest caveats. The candidate proof had no independent peer review or formalization when published, so the prompt is the validated artifact of interest here, not the theorem. And no public ablation isolates which prompt elements carried the result; the mechanism-level evidence comes from the independent research in the research evidence reference.

Vendor Doctrine

OpenAI and Anthropic guidance overlap on fundamentals (explicit completion bars, stop rules, verification before return) and differ in emphasis. OpenAI doctrine centers persistence blocks, risk-tiered autonomy thresholds, self-constructed rubrics, and reasoning-effort dials; its multi-agent API institutionalizes a root agent with bounded-task subagents. Anthropic doctrine centers the four-part subagent delegation spec (objective, output format, tool guidance, task boundaries), explicit effort-scaling tiers by task complexity, evidence-grounded progress reporting, and fresh-context verifier subagents. Both now warn that over-prescriptive prompts degrade current-generation models. Dated extracts with sources are in the vendor guidance reference.

Generalizing Beyond Mathematics

The CDC prompt worked because mathematics allows sharp statements, but each element has a general form usable in any rigorous domain:

The transformation workflow for a scientist or engineer with a hard problem: state what a complete answer would let them do, work backward to the predicate that enables it, then spend most of the effort listing what they would refuse to accept from a junior collaborator. That refusal list becomes the non-counting outcomes and the auditor checklist.

Practical Guidance