KBLaM reconciliation

Type: agentic-system-analyses/types/agentic-system-reconciliation-report.md

Reconciliation

Boundary and duplicate identities

All four reports share the boundary and its Source register. SRC-1 supplies implementation and doctrine/design, not observed runs or causal experiments. This reconciliation uses the pinned members and their retained passages; no source rereads were needed. No analyst correction answers or declined objections were supplied. Changes in the supplied reports are assessed below without treating the previous reconciliation as established findings.

Amendment: EPI-OBJ-entity is superseded by MEM-OBJ-entity. Both identify the Entity dataclass's name, description, objectives and purpose values, held in Python and appended JSONL, at SRC-1, src/kblam/utils/data_utils.py and dataset_generation/gen_synthetic_data.py. The epistemic declaration supplies the schema passage; memory supplies parsing and persistence passages. Imported versus generated instances do not establish different object kinds. Findings about fictional assignments, unknown source warrant and missing historical provenance continue to concern this same referent; supersession changes neither report's findings.

Amendment: EPI-OBJ-reference-judgment is superseded by MEM-OBJ-reference-judgment. Both identify the script-local EvalExample in SRC-1, experiments/output_scorer.py: text/true_answer/score, numeric parsing, an in-memory batch list, post-batch JSONL saving and mean reporting. Both retain the dataclass and float-conversion passages. Their evaluator domain is reference agreement, not independent factual confirmation. The epistemic reporting/retention assessment and memory's admission, append and I/O limits remain applicable to the same object.

Amendment: EPI-OBJ-open-judgment is superseded by MEM-OBJ-open-judgment. Both identify the evidence/question/response/score/reason EvalExample in SRC-1, experiments/output_scorer_open_ended.py. The container schema and EPI-RTE-open-judgment's parsing/append passages establish the shared payload and production chain. Both distinguish per-record retention from normal completion's implicit None and exception-prefix return. The epistemic evidence-relative/common-sense license and memory's rationale, persistence and missing later-reader findings remain attached to this same part. Neither amendment merges this judgment with the scalar reference judgment.

Containers, parts and ruled-out supersessions

MEM-OBJ-data-point encompasses retained knowledge and supervision rows, including optional extensions. EPI-OBJ-data-point identifies ordinary fields, while EPI-OBJ-extended-qa isolates model-written extensions and perturbed questions. These scopes are not identical. The ordinary Q field can itself be rewritten; the records distinguish production and consumption rather than guaranteeing that every ordinary Q remains template-produced. Their passages and EPI-RTE-augment-qa support these distinctions. Neither epistemic record alone supersedes the encompassing memory row, and the memory container's differentiated findings do not require split supersession.

MEM-OBJ-eval-example includes exported answers and derived judgments. EPI-OBJ-eval-example groups the two script-local judgment dataclasses. The containers overlap but are not identical; neither supersedes the other. Both now have separately declared reference and open-ended parts, connected by the amendments above. The containers explicitly distinguish the operative parts rather than assigning them one schema, evaluator or reporting path. Their findings remain valid as container findings, so no split supersession is asserted. EPI-OBJ-model-output includes decoded answers and refusals before or after export; it is not identical to either judgment container or to just their exported-input portion.

MEM-OBJ-encoder is retained parameter state, not the live RT-CMP-kb-encoder. MEM-OBJ-kblam-config is symbolic control, not numerical weights. MEM-OBJ-embd-key and MEM-OBJ-embd-value have distinct source fields, projectors and attention roles. MEM-OBJ-attention-weights comprises experiment snapshots and derived metrics, not knowledge vectors or generation-cache state. These object/component comparisons rule out supersession; production or consumption by a runtime route does not make an object the route itself.

EPI-RTE-template-qa and EPI-RTE-augment-qa are distinct declared parts of RT-RTE-dataset-production, not duplicate whole routes. Formatting and model rewriting have different semantic licenses. Neither replaces the parent's entity generation, embedding and split functions. Likewise EPI-RTE-answer-cleanup and EPI-RTE-metric-inputs are distinct parts of RT-RTE-evaluation-modes: one changes extracted text, the other population and measurement. The parent remains a multi-function container. The separately launched EPI-RTE-open-judgment adds detailed coverage of the other scorer, which runtime explicitly leaves unassessed; it does not supersede the reference-scoring operation or its broader runtime container. The judgment objects are products of those routes, not duplicate routes.

MEM-RTE-result-cleanup spans outputs of RT-RTE-accuracy-generation and the evaluation modes, but governs sweep derivation, directory deletion and final reporting. Its progression is distinct from generation and scoring, ruling out supersession of either runtime route. Its SRC-1, experiments/eval.py deletion passage limits the retention horizon of attention snapshots. Same-call reload, per-iteration deletion and later summary conversion are separate consumers and horizons. Output/log-directory aliasing and failure before the final JSON write remain limits; this is not a claim that every accuracy invocation deletes its outputs.

Independent convergence and current integration dispositions

The previous CLI-reachability conflict is no longer present in these inputs. RT-RTE-accuracy-generation and the workflow account now explicitly carry the aug_row import failure, before dispatch and even without fancy questions. Runtime retains the import and actual augment_row definition passages at SRC-1, experiments/eval.py and src/kblam/utils/data_utils.py. Memory's accuracy annotation reports the same defect, and its Integration issues recognizes the runtime correction. Epistemic accuracy findings cite that now-qualified runtime record rather than requiring unchanged CLI reachability. Their positive internal-wiring accounts therefore no longer depend on a record that omits the entry blocker. Runtime's wired implementation status and uninspected operation status do not assert successful entry; source repair remains necessary.

Workflow annotates that same runtime route rather than declaring a duplicate. It adds uncached equal-size range/index-shape incompatibility, expected layer files, top-five and batch-size preconditions, and sequential partial delivery. These are additional limits, not competing findings: runtime's selection-policy description does not guarantee that every branch completes. Both reports characterize row-position attention ranking as measurement rather than factual acceptance, and distinguish function return from durable CLI files. No report establishes actual delivery or automatic recovery.

The previous open-ended aggregate-reporting conflict is also no longer present. MEM-OBJ-eval-example, its open-ended part and EPI-RTE-open-judgment now agree: per-record append precedes completion; normal completion returns None; an exception can return an accumulated prefix. The exception-return and main-block mean passages retained by epistemic, together with both reports' complete-file inspection findings, prevent a claim of normal completed-batch mean reporting while preserving source-wired per-record retention. Reference scoring has a separate list-return/save/mean path. Both defaults' GPT4 incompatibility, absent range enforcement and unobserved persistence/calibration remain separate limits.

The previous missing-judgment-parts issue no longer applies. Both members now declare and assess reference and open-ended judgment objects under their retained containers. The operative identity mapping is stated above, not left to a shared label or schema caveat. A scalar reference score lacks a retained provider reason; an open-ended judgment retains parsed reason text. Neither member establishes a shipped later inference reader for those reasons. The source-wired retention of rationale therefore coexists with unestablished later behavioral use and does not become a uniform judgment-container property.

RT-RTE-kb-attention, its memory annotation and the epistemic ledger converge on numerical influence through RT-BAP-knowledge-attention, not guaranteed factual correctness. Internal Llama top-k selection qualifies a broad no-selection interpretation of RT-CLM-knowledge-augmentation, without establishing an external retrieval service. Phi's distinct branch and Llama's score-slicing limit remain local. The conditional bypass behind RT-CLM-base-preservation does not establish universal exact-output equality. No report supplies observed grounding, scaling benefit or exact-output equivalence.

RT-RTE-adapter-training, memory's write/read-back account and the epistemic ledger connect supervised updates, periodic checkpoint retention and later load wiring through RT-BAP-checkpoint-consumption. They retain the save/resume layout mismatch and distinguish optimization from validated capacity improvement, formulated criticism and exercised self-improvement. Memory's independent-resampling passage at SRC-1, experiments/train.py qualifies refusal-target membership; runtime does not guarantee disjointness, so this is an added limit rather than disagreement.

Memory's direct-inference annotation adds unresolved tensor/list type and layout compatibility to RT-RTE-direct-inference's defective convenience loader. Runtime's afforded interface, its separately wired compatible direct-tensor consumer and epistemic's implemented content-transformation assessment concern different scopes. None establishes successful public-processor execution, and no strongest-status selection is made. Historical text/vector alignment, checkpoint/query-head provenance and opaque provider retention remain scoped unknowns, not conflicts. Authentication persistence on RT-CMP-gpt-session is service-access metadata, not established semantic memory.

Remaining cross-report disagreement

Unresolved conflict: RT-RTE-dataset-production, EPI-RTE-augment-qa and the memory dataset-production annotation retain different qualifications of evidence-only extension guidance. Runtime says the knowledge guidance requests QA based only on supplied information without distinguishing delivery for that extension call. Epistemic explicitly qualifies that sentence as intended, not delivered, guidance; memory's Integration issues requests the same distinction. Their passages at SRC-1, dataset_generation/gen_synthetic_data.py and src/kblam/gpt_session.py show assignment to system_prompt while inherited generate_response consumes system_msg. Runtime's sentence establishes that the guidance text requests this behavior; the specialists do not dispute its existence, but reject reading it as a wired constraint on extension calls. This prevents an integrated operational-guidance claim that the evidence-only system constraint reaches the provider through that call. It does not prevent an account of the declared request, structural admission or field persistence, and does not establish absent model compliance. The reconciler neither rewrites runtime's finding nor adjudicates whether its present wording sufficiently confines the claim to declaration.

No additional disagreement is established by the supplied integration issues. Cleanup, input compatibility, outlier membership and lineage limits are connected above as additions; the CLI and judgment-part corrections remove their prior dependencies and conflicts. No declined analyst objection was supplied to adjudicate. The verifier owns judging the remaining guidance qualification and addressing any required correction to its declaring analyst.