Intent Refinement During Execution: Adaptive Objectives Without Re-Instantiation

Mechanism

The mechanism operates inside the lifecycle of a persistent semantic object resident in shared memory. Each such object carries an intent field, a state field, a lineage record, and a policy reference. When the executing agent encounters an ambiguity that prevents deterministic progress, it does not silently substitute a default and continue, nor does it abort and discard partial work. Instead, it issues a directed refinement query addressed to the operator (or to the operator's designated delegate), structured against the schema declared in the intent field itself.

A directed refinement query is not a free-form prompt. It identifies the specific ambiguous slot, enumerates the candidate resolutions consistent with the current state, and indicates which downstream branches each resolution would enable or foreclose. The operator's response is parsed against the same schema and merged into the intent field as a structured update rather than as additional text. The merge is performed in place: the semantic object is not destroyed and recreated, no new identifier is assigned, and no prior progress is discarded. The lineage record receives an append-only entry capturing the query, the response, the resulting field delta, and a cryptographic commitment to the prior intent state.

Cycle-freedom is enforced structurally. The refinement subsystem maintains a directed acyclic graph of intent slots, where each slot may depend on the resolution of earlier slots but cannot depend on its own future resolution. Refinement queries traverse this graph in topological order. A slot once resolved cannot be reopened by a subsequent refinement on a downstream slot; if downstream evidence implies that an earlier resolution was incorrect, the system raises a higher-order exception rather than silently looping. This eliminates the failure mode in which an agent oscillates indefinitely between alternative interpretations.

Boundedness is enforced by a refinement budget carried in the intent field. The budget specifies a maximum number of refinement rounds, a maximum cumulative latency, and a maximum semantic distance between the original intent and any refined version. When any of these limits is approached, the agent transitions the object to a deterministic terminal state — typically partial completion with a clearly marked unresolved-slot annotation — rather than continuing to demand operator attention. Boundedness ensures that intent refinement cannot become a denial-of-service vector against the operator and cannot mask a fundamentally underspecified request as ongoing productive work.

Operating Parameters

The refinement budget is expressed as a tuple (R, T, D) where R is the maximum number of refinement rounds permitted in a single execution, T is the cumulative wall-clock interval over which refinement may remain pending, and D is the maximum semantic divergence between the original intent vector and any candidate refined intent vector. Typical operating ranges observed in deployment span R = 1 to 8 rounds, T = 30 seconds to 72 hours, and D bounded in cosine distance to a value between 0.15 and 0.40 against the original intent embedding.

Latency targets for the agent-side portion of refinement (query construction, schema validation, lineage commit, and merge) are typically held under 250 milliseconds per round on commodity hardware, with the dominant cost being the cryptographic commitment to lineage. Operator-side latency is unbounded by the agent but is bounded by T; if the operator does not respond within the window, the bounded terminal state is entered without operator action. Throughput at the substrate level is governed by the rate at which lineage append operations can be committed; reference deployments sustain 10,000 refinement rounds per second per substrate node when lineage commits batch at one-second intervals.

Cycle detection runs in O(V + E) in the size of the intent slot graph, which is bounded by the schema and is typically small (fewer than 64 slots even for complex requests). Cycle detection is therefore not on the critical performance path. Semantic-distance computation is O(d) in the embedding dimension and runs in microseconds.

Failure modes are explicit and enumerated. Budget exhaustion produces a refinement-bounded terminal marker. A schema-incompatible operator response produces a refinement-rejected marker without consuming a refinement round. A cycle attempt produces a refinement-cycle-blocked marker and halts further refinement on the affected slot. Each marker is itself an entry in the lineage and is auditable downstream.

Alternative Embodiments

In a single-operator interactive embodiment, refinement queries surface synchronously in the operator's user interface and are answered by direct selection or short text input. The semantic object remains resident in memory throughout, and the operator perceives the refinement as a natural disambiguation dialogue rather than as a system error or restart.

In a multi-operator delegated embodiment, the intent field designates a chain of refinement authorities. The agent first queries the primary operator; on timeout or explicit forwarding, the query escalates to a designated delegate. Each authority's response is bound to its identity in the lineage, allowing later audit to determine which party resolved which ambiguity. This embodiment is appropriate for organizational settings in which the original requester may be unavailable but a downstream colleague holds the necessary domain knowledge.

In an autonomous-policy embodiment, the operator pre-authorizes a refinement policy that resolves common ambiguities without interactive query. The policy is itself a structured document carried by the semantic object, and refinements drawn from it are still recorded in the lineage but consume no operator attention. This embodiment is appropriate for high-volume routine work where operator availability would otherwise become the bottleneck.

In a federated multi-tenant embodiment, refinement queries may cross organizational boundaries when the semantic object's execution traverses multiple administrative domains. The intent field carries the cryptographic identity of the originating operator, and refinement responses from non-origin parties are flagged as advisory rather than authoritative unless explicitly delegated. This preserves the integrity of the intent record across trust boundaries.

In an offline-tolerant embodiment suitable for edge deployment, refinement queries may be queued locally when the operator is unreachable, with the semantic object entering a suspended state rather than a terminal state. On reconnection, queued queries are surfaced in batched form and resolved together, after which execution resumes from the suspension point.

Composition with Other Mechanisms

Intent refinement composes with the lineage commitment mechanism by sharing the same append-only structure. Every refinement event produces a lineage entry that is indistinguishable in form from any other state-transition entry, which means downstream auditors and verifiers do not require a separate code path to reason about refined versus original intent. The cryptographic chain is unbroken across refinement events.

Intent refinement composes with the policy enforcement mechanism by treating each refinement as a policy-relevant event. The policy reference attached to the semantic object is consulted at every refinement round, and the policy may forbid certain refinements (for example, a policy may prevent broadening the intent's data-access scope, or may require a higher-tier operator's approval before a refinement crosses a sensitivity boundary). Policy decisions on refinements are themselves logged in the lineage.

Intent refinement composes with the migration mechanism by remaining valid across substrate transitions. Because the intent field, the lineage, and the refinement budget all travel with the semantic object as part of its in-memory representation, a migrated object can continue to refine intent on a new node without re-establishing context with the operator. The operator perceives a continuous interaction even when the underlying execution has moved.

Intent refinement composes with the determinism guarantee by producing identical outcomes given identical refinement traces. Because the lineage records the exact sequence of queries and responses, replaying execution from a stored lineage produces a bit-identical semantic object, which is essential for audit, debugging, and dispute resolution.

Prior-Art Distinctions

Conventional clarification dialogues in conversational systems treat each operator turn as additional context to be appended to a growing prompt, with no structured intent field, no bounded refinement budget, and no cycle-freedom guarantee. The system can re-ask the same question indefinitely, can drift the operator's apparent intent through cumulative paraphrase, and provides no auditable record distinguishing original intent from later modification. Memory-resident intent refinement differs in that the intent is a typed, versioned field with explicit slot structure, refinement is bounded by an explicit budget, cycles are structurally precluded, and every refinement event is committed to an append-only lineage.

Workflow systems that support task re-instantiation handle changing requirements by destroying the original task instance and creating a new one with the modified parameters. This loses partial progress, breaks identity continuity, and produces a fragmented audit trail. The disclosed mechanism modifies intent in place on a persistent semantic object, preserving identity and partial progress while still recording the modification.

Form-based clarification interfaces require the system to enumerate all possibly ambiguous fields up front and to demand resolution before any work begins. The disclosed mechanism allows execution to proceed under the original intent and to issue refinement queries only when ambiguity actually blocks deterministic progress, dramatically reducing operator burden on requests that turn out to be unambiguous in practice.

Reinforcement-learning-from-human-feedback approaches refine model behavior across many sessions by aggregating preference signals; they do not refine the intent of a single in-flight execution and do not preserve a per-object lineage of refinement events. The disclosed mechanism is per-object and synchronous with execution.

Disclosure Scope

The disclosed mechanism encompasses any system in which (i) a persistent semantic object resident in addressable memory carries a structured intent field, (ii) execution may pause to issue a directed refinement query against an explicit slot in that field, (iii) refinement is bounded by an explicit budget over rounds, time, and semantic divergence, (iv) refinement is cycle-free with respect to a topologically ordered slot graph, and (v) every refinement event is recorded in an append-only cryptographically committed lineage that travels with the object.

The disclosure is not limited to any particular embedding model, schema language, transport protocol, user-interface modality, or substrate type. It applies equally to text, structured data, multimodal, and tool-invocation intents. It applies whether the operator is a human, another autonomous agent, or a chained system of agents subject to the same disclosure. The cryptographic primitives used for lineage commitment are not restricted to any particular hash function or signature scheme.

The disclosure covers operations on a single substrate, on a federation of substrates under common administrative control, and on independently administered substrates that interoperate through the disclosed protocols. It covers synchronous interactive refinement, asynchronous queued refinement, and pre-authorized policy-driven refinement, and any combination thereof within a single semantic object's lifecycle.