25 KiB
Engram Prior-Art Scan
Dated 2026-08-12. This is an engineering novelty read, not legal advice. It is intended to feed a patent/whitepaper priority decision by identifying which claims sit in a clean lane and which are wholly or partly anticipated by existing work. A patent attorney and a formal search (USPTO/Google Patents/Espacenet) should confirm before filing. Where a claim is anticipated, this document says so plainly — the goal is honest scoping, not inflated novelty.
How to read this
Engram is an immutable, temporally-provenanced knowledge graph (tombstone-not-delete, supersede-not-overwrite; every node carries created_at + superseded_at + provenance). Over that graph it computes geometry descriptors G = (centroid, covariance/ellipsoid, skeleton graph, membership weights) in a joint embedding+graph space, and runs named operators (overlap, combine, distance, difference, analogy=Procrustes, traverse=geodesic) over them. A "self" is a reified geometry. Time-travel is a query filter (created_at ≤ T < superseded_at), not a transaction-log replay. "Self-occupation" reconstructs the self-geometry/knowledge-state as of T, locks it read-only, and converses with it with all post-T data masked.
The recurring pattern in the findings below: every individual primitive is prior art. Bitemporal reconstruction, memory streams, vector-symbolic composition, geometric KG operators, hindsight-leakage auditing, embedding drift detection — all exist and are well-published. Novelty, where it exists, lives in specific integrated mechanisms, and must be claimed narrowly against those primitives. Broad claims ("reasoning as geometry," "reconstruct what was known at T," "detect drift by distance") will be rejected on sight.
(a) Hindsight-free decision auditing via immutable temporal knowledge-state reconstruction + future-masked occupation
Claim (restated narrowly). A method for auditing a past decision by (1) constructively reconstructing the exact knowledge-state a decision-maker held at time T from an immutable, tombstone+supersede provenance graph (selecting nodes live-at-T via created_at ≤ T < superseded_at), (2) recomputing the derived concept/self geometry over only that live-at-T slice, and (3) presenting that reconstructed state read-only, with all post-T nodes masked, as the sole evidentiary basis for judging the decision — such that the reconstruction is tamper-evident because nothing is ever overwritten or deleted.
Closest prior art.
- HindsightBench (Aug 2026) — a black-box behavioral audit protocol that detects parametric hindsight in time-indexed LLM decision tasks by manipulating the asserted date in the prompt (Revealed/Date-only/Masked/Transplant arms) and measuring behavioral shift. It explicitly does not reconstruct a knowledge state from provenance; it does not require corpus access or logprobs. It also reports that instructed forgetting fails — a 52% performance gap vs. true ignorance. https://arxiv.org/abs/2607.18867
- Agentic Time Machine (Jun 2026) — wraps web tools with a leakage filter that blocks post-cutoff or answer-revealing content before it reaches the agent (for forecasting benchmarks). https://arxiv.org/pdf/2606.21013
- Zep/Graphiti — bitemporal KG memory that can answer "what was the user's plan in January?" via point-in-time recall over event-time + ingestion-time. https://arxiv.org/abs/2501.13956 , https://www.getzep.com/ai-agents/temporal-knowledge-graph/
- Auditable clinical-AI provenance frameworks — immutable, timestamped "source-to-decision" trails recording the exact evidence shown to a clinician, for FDA transparency/liability. https://pmc.ncbi.nlm.nih.gov/articles/PMC12913532/
- Hindsight-bias clinical literature — retrospective case-note review is critically distorted by outcome knowledge; reconstructing the decision-maker's past perspective is the known mitigation. https://www.researchgate.net/publication/330427526 , https://kevinmd.com/2026/03/how-hindsight-bias-distorts-clinical-medicine.html
What is genuinely differentiated. The primitives — bitemporal point-in-time recall (Zep), immutable evidence trails (clinical AI), hindsight-bias mitigation by perspective reconstruction (clinical psych), leakage filtering (Agentic Time Machine), hindsight auditing (HindsightBench) — are all taken. What appears unclaimed is the specific combination: constructive knowledge-state reconstruction from an immutable tombstone+supersede graph, used as the affirmative evidentiary substrate for judging a decision, where correctness of the future-mask is guaranteed by the data model (a node is either live-at-T or it is not) rather than by prompt instruction or a heuristic content filter. HindsightBench and Agentic Time Machine both operate on the model's contaminated parametric memory and fight leakage behaviorally/heuristically; Engram sidesteps parametric leakage by making the evidence set itself provably T-clean and then recomputing geometry over it. The tamper-evidence-by-construction angle (append-only provenance ⇒ the reconstruction cannot be silently backdated) is also not present in the behavioral-audit line.
Scoped-claim recommendation. Claim the pipeline, not the goal: "reconstructing a decision-maker's knowledge-state as of T by selecting live-at-T nodes from an append-only tombstone+supersede provenance graph and recomputing derived concept/self geometry over that slice, then serving it read-only with post-T nodes masked as the evidentiary basis for decision review." Anchor on (i) constructive reconstruction from immutable provenance (not prompt-based date assertion), (ii) mask-correctness guaranteed by the data model, (iii) recomputed geometry (not just fact recall) as the reconstructed state. Do not claim "hindsight-free auditing" broadly, "point-in-time recall," or "immutable audit log" — all taken.
Verdict: PARTIALLY TAKEN (the goal and every primitive are taken; the constructive-reconstruction-from-immutable-provenance-as-evidentiary-substrate integration looks clean if narrowly scoped).
(b) Drift detection via geodesic displacement of an anchored self-geometry
Claim (restated narrowly). A method that reifies an agent's "self" as a geometry descriptor with a designated stable value-core anchor and a mutable periphery, and classifies change by decomposition: extension of the periphery (core displacement ≈ 0) is scored as growth, whereas geodesic displacement of the core is scored as corruption — measured as geodesic distance between self(now) and the anchored self(reference) on the graph+embedding manifold.
Closest prior art.
- Embedding / concept-drift detection — mature field: distribution-distance of embeddings, per-label distributions (Drift Lens), K-core-distance from a dense "core" of baseline logic, growing average distance from baseline anchors as the drift signal. https://www.evidentlyai.com/blog/embedding-drift-detection , https://ieeexplore.ieee.org/iel7/9679833/9679835/09679880.pdf , https://www.sciencedirect.com/science/article/pii/S0925231225018624
- Agent identity/goal-drift governance — identity-hash functions over characteristic behavior for drift detection; "dominant" core persona preventing fragmentation; reflection-based long-term self-model evolution vs. short-term compensation. https://arxiv.org/pdf/2604.14717 (Layered Mutability) , https://www.researchgate.net/publication/397950116 (Agent Goal Drift in Stateful Systems)
- Persistent Identity multi-anchor architecture — explicit anchors for resilient agent identity/memory continuity. https://arxiv.org/pdf/2604.09588
What is genuinely differentiated. "Distance from an anchored baseline core = drift" is squarely prior art (K-core-distance, baseline-anchor distance growth). Agent-identity work already has core-vs-drift and anchors. What is not obviously present is the core/periphery decomposition of drift into two distinct, oppositely-valenced outcomes on a reified self-geometry — i.e., using a geometric self-model (centroid + covariance/ellipsoid + skeleton) where growth is formally "periphery ellipsoid expands while core centroid/anchor stays fixed" and corruption is "core centroid/anchor is geodesically displaced." Existing drift work treats all displacement as drift (bad); it does not carve legitimate growth from corruption via a fixed value-core on a self-geometry. The specific formalization — geodesic (graph-aware, non-Euclidean) displacement of a pinned value-core sub-geometry vs. free peripheral expansion — is the differentiator.
Scoped-claim recommendation. Claim "detecting agent value-corruption by measuring geodesic displacement of a pinned value-core sub-geometry of a reified self-geometry, while treating expansion of the peripheral geometry with a stationary core as non-corrupting growth." Emphasize (i) the self is a geometry descriptor with an explicitly designated immutable core anchor, (ii) growth vs. corruption is a decomposition (two signals), not a threshold on one distance, (iii) geodesic/graph-aware metric. Do not claim "drift detection by embedding distance" or "anchored baseline comparison" — taken.
Verdict: PARTIALLY TAKEN (distance-from-anchor drift is taken; the growth/corruption core-vs-periphery decomposition on a reified self-geometry is the narrow clean sliver — and it's the weakest/most crowded of the five).
(c) Reasoning as composable geometry operations over a persistent temporally-provenanced graph
Claim (restated narrowly). A reasoning method in which inference steps are explicit, named, first-class operators (overlap, combine, distance, difference, analogy=Procrustes alignment, traverse=geodesic) applied to geometry descriptors computed over a persistent, immutable, temporally-provenanced knowledge graph — such that each reasoning step is individually inspectable, logged with provenance, and replayable against a past graph state; as distinct from implicit, unnamed activation/attention transforms inside a neural net.
Closest prior art.
- Vector Symbolic Architectures / HRR / SDM (Plate, Kanerva) — the canonical "algebra over vectors": binding, bundling/superposition, permutation, similarity; explicitly compositional/symbolic reasoning via vector operations. This is the strongest prior art for "named composable operators over vectors." https://www.emergentmind.com/topics/holographic-reduced-representations-hrrs , https://arxiv.org/pdf/2512.14709 (Attention as Binding)
- Geometric KG query embeddings (Query2Box-lineage) — reasoning as named geometric operators (projection, intersection) over box/region embeddings; geometric multi-hop reasoning; geometry-interaction KG embeddings. https://arxiv.org/html/2505.12369v2 , https://ojs.aaai.org/index.php/AAAI/article/view/20491/20250
- Geometry-of-reasoning / embedding-space reasoning — CoT as trajectories/flows in representation space; vector algebra + manifold geometry for deduction/induction/analogy. https://arxiv.org/abs/2510.09782 , https://arxiv.org/pdf/2504.02018
- Riemannian knowledge manifolds — geodesics as shortest semantic paths with a convergent geodesic solver. https://arxiv.org/html/2606.05907v2
- Neuro-symbolic propose-verify — explicit symbolic operations + solver verification.
What is genuinely differentiated. "Reasoning as composable vector/geometry operations" is thoroughly prior art — VSA/HRR own the compositional-operator framing; Query2Box owns named geometric operators (projection/intersection) for KG query answering; geodesic traversal over semantic manifolds is published. The individual operators (overlap≈intersection, distance, geodesic-traverse, Procrustes-analogy) each exist. The candidate differentiator is not any operator and not "geometry as reasoning" — it is the coupling of the operator calculus to the immutable temporal-provenance substrate: every operator input is a live-at-T geometry, every step is provenance-stamped, and the whole derivation is replayable against a reconstructed past graph state (i.e., operator-level temporal reproducibility + auditability). VSA/Query2Box run over static/atemporal embedding stores with no provenance and no time-travel; geometry-of-reasoning work is about a neural net's internal trajectory, not an external audited calculus. So the calculus itself is taken; "an auditable, replayable geometry calculus whose operands are temporally-reconstructed geometries" is the narrow lane.
Scoped-claim recommendation. Do not claim a "calculus of thought," "reasoning as geometry," or any specific operator (overlap/difference/geodesic/Procrustes) — all taken. Claim only the integration: "an audit trail in which each named geometric reasoning operator is provenance-stamped and its operands are geometry descriptors reconstructed from an immutable temporal graph as-of a query time, enabling deterministic replay of a reasoning derivation against a past knowledge-state." The defensible novelty is temporal reproducibility + provenance of the operator chain, not the operators.
Verdict: TAKEN (as "reasoning as composable geometry ops" — VSA/HRR + Query2Box + geometry-of-reasoning fully occupy it). Only the auditable/replayable-over-immutable-temporal-substrate framing survives, and it survives as a thin sliver of (a)/(e), not as an independent claim.
(d) Self-occupation with engineered future-masking
Claim (restated narrowly). A method for reasoning as a past self: reconstruct the self-geometry and knowledge-state as of T from the immutable provenance graph, rigorously enforce the created_at ≤ T cut at the data layer (all post-T nodes structurally excluded, not instructed-away), lock the reconstruction read-only, and drive a conversational/reasoning session that is provably uncontaminated by hindsight — the mask being a property of the substrate, not of a prompt or the model's willingness to "forget."
Closest prior art.
- HindsightBench — establishes the problem rigorously and shows that prompt-level "pretend it's
T" fails badly (instructed forgetting ≠ ignorance; 52% gap; date assertions obeyed but hindsight still leaks). This is the strongest adjacent art and, helpfully, motivates Engram's substrate-level approach rather than anticipating it. https://arxiv.org/abs/2607.18867 - Agentic Time Machine — closest mechanism: a leakage filter blocking post-cutoff content before it reaches the agent. But it filters tool outputs heuristically for a forecasting benchmark; it does not reconstruct and occupy a reified past self/knowledge-state. https://arxiv.org/pdf/2606.21013
- Causal Agent Replay — counterfactual replay/attribution of agent failures (replay, but not future-masked past-self occupation). https://arxiv.org/abs/2606.08275
- Chronologically-consistent pretraining / counterfactual-anchored decoding / forget-retain logit adjustment — model-internal mitigations of parametric leakage (named in HindsightBench). Different layer entirely.
What is genuinely differentiated. The field is actively fighting hindsight leakage at the model layer (pretraining, decoding, logit surgery) and at the tool-output layer (heuristic leakage filters). Engram's move is orthogonal and, per HindsightBench's own findings, addresses the failure mode the field just documented: enforce the cut at the evidence/data layer via an immutable time-indexed graph, so the "past self" is a reconstructed read-only geometry whose accessible universe is exactly the live-at-T slice. No source found reconstructs a reified self-geometry as of T and converses with it as a first-class object. The differentiators: (i) the masked entity is a reconstructed self, not just filtered context; (ii) mask correctness is structural (a node's created_at either satisfies the cut or the node is absent) rather than heuristic/instructed; (iii) it is tamper-evident via append-only provenance. Note the residual honesty caveat: if the underlying LLM used for the conversation has parametric hindsight, Engram's substrate-clean evidence does not fully neutralize it — the claim must be about the evidence/state being T-clean, which is the part Engram genuinely controls.
Scoped-claim recommendation. Claim "reconstructing a reified agent self-geometry and knowledge-state as-of T from an append-only temporal provenance graph and conducting a read-only reasoning/conversation session over it in which the accessible node universe is structurally restricted to the live-at-T slice (data-layer future-masking), yielding a T-clean evidentiary state." Lean on structural (data-model-guaranteed) masking vs. instructed/heuristic masking, and on the reified-past-self object. Explicitly scope to the evidence-state cleanliness (not a claim that the LLM has zero parametric leakage). Do not claim "prevent hindsight in LLMs" or "leakage filtering" broadly.
Verdict: CLEAN LANE (narrowly — data-layer/structural future-masking over a reconstructed reified past self is not occupied; adjacent art is behavioral-audit, tool-output filtering, or model-internal mitigation. This is the strongest of the five, precisely because HindsightBench shows the prompt-level approach fails and no one is doing substrate-level self-reconstruction).
(e) Query→geometry temporal reconstruction with NO transaction logs
Claim (restated narrowly). Reconstructing a past knowledge-state as a query-time filter over immutable, per-node timestamped provenance (created_at ≤ T < superseded_at) followed by recomputation of the geometry descriptors over that slice — with no event/transaction log and no periodic snapshots; the immutable per-node provenance is the temporal record, and derived geometry is recomputed rather than stored/replayed.
Closest prior art.
- Bitemporal databases (XTDB, et al.) / event sourcing — "as-of" point-in-time queries over valid-time + transaction-time; immutability as audit log. Critically, XTDB describes reading bitemporal data as a process "similar to event sourcing… playing through the history… in reverse system-time order" — i.e., the mainstream bitemporal model is replay/reconstruction-through-history. https://v1-docs.xtdb.com/concepts/bitemporality/ , https://www.juxt.pro/blog/value-of-bitemporality/
- Zep/Graphiti — bitemporal (event-time T + ingestion-time T′) fact validity + supersession chains; point-in-time recall. https://arxiv.org/abs/2501.13956
- TKG reasoning frameworks / ElephantBroker-class runtimes — "immutable fact store, all temporal weighting applied at query time; facts created after the query timestamp excluded, facts superseded after query timestamp treated as current; invalidate by writing
t_invalidrather than delete." This is very close to Engram's filter and supersede/tombstone semantics. https://www.emergentmind.com/topics/temporal-knowledge-graph-reasoning-tkgr , https://arxiv.org/pdf/2603.25097 - Numerous bitemporal/immutable-DB patents (point-in-time reconstruction, retroactive/historical transactions). e.g. US 11,935,046; US 8,812,512 (via USPTO search) — a patent attorney must clear these.
What is genuinely differentiated. The temporal filter (created-before, superseded-after) and tombstone-not-delete / supersede-not-overwrite are standard bitemporal KG practice — Zep and the TKGR frameworks describe almost exactly this. So the reconstruction-by-filter primitive is TAKEN, and "immutable provenance instead of a mutable audit log" is TAKEN (that's the bitemporal value prop). The only thing that is not standard: what gets reconstructed is not just a set of facts/edges but a set of derived geometry descriptors (centroid/covariance/skeleton/membership) recomputed over the live-at-T slice — i.e., recompute-geometry-on-read rather than store-and-replay. Bitemporal DBs reconstruct records; Engram reconstructs derived manifold structure. The "no transaction log / no snapshot — provenance IS the temporal record, geometry is recomputed" framing is a design stance that is defensible only if paired with the geometry recomputation; on its own it is indistinguishable from XTDB/Zep.
Scoped-claim recommendation. Do not claim bitemporal reconstruction, "as-of" queries, tombstone/supersede, or "immutable provenance as audit record" — all squarely taken (Zep, XTDB, TKGR, patents). Claim only: "reconstructing a derived geometry descriptor set (centroid/covariance/skeleton/membership) for a past knowledge-state by recomputing it on-read over the live-at-T node slice, without storing per-T geometry snapshots or a geometry-mutation log." The novelty is geometry-recompute-on-read over a bitemporal slice, not the slice.
Verdict: TAKEN (as "query-filter temporal reconstruction over immutable provenance" — Zep + XTDB + TKGR own it outright). Only "recompute derived geometry on-read, snapshot-free" survives, and it is really a facet of (c)/(a) rather than an independent claim.
Summary
| Claim | Verdict | Narrowest defensible (clean-lane) framing |
|---|---|---|
| (a) Hindsight-free decision auditing via reconstructed knowledge-state + future-masked occupation | PARTIALLY TAKEN | Constructive knowledge-state reconstruction from an append-only tombstone+supersede graph, used as the affirmative evidentiary substrate for decision review, with mask-correctness guaranteed by the data model and tamper-evidence by construction — not prompt/date-assertion (cf. HindsightBench) and not tool-output filtering (cf. Agentic Time Machine). |
| (b) Drift as geodesic displacement of anchored self-geometry | PARTIALLY TAKEN | Growth-vs-corruption decomposition of change on a reified self-geometry via geodesic displacement of a pinned value-core sub-geometry vs. free peripheral-ellipsoid expansion. (Crowded; weakest lane.) |
| (c) Reasoning as composable geometry ops over a temporal graph | TAKEN | Only survivor: provenance-stamped, replayable operator chain whose operands are geometries reconstructed as-of a query time (temporal reproducibility of the derivation) — never the operators or "geometry as reasoning" themselves. |
| (d) Self-occupation with engineered future-masking | CLEAN LANE (narrow) | Reconstruct a reified past self-geometry and converse with it read-only, with the accessible node universe structurally restricted to the live-at-T slice (data-layer masking) — not instructed forgetting (which HindsightBench shows fails) and not heuristic content filtering. |
| (e) Query→geometry temporal reconstruction, no transaction logs | TAKEN | Only survivor: recompute derived geometry descriptors on-read over the live-at-T slice, snapshot-free — never the bitemporal filter, tombstone/supersede, or "immutable provenance as record," all of which Zep/XTDB/TKGR own. |
Overall posture
Broad claims over primitives will be rejected. Each of the five candidate claims decomposes into (i) a primitive that is unambiguously prior art and (ii), in three of five cases, a thin integrated mechanism that appears unclaimed. The prior art is strong and specific: Zep/Graphiti and XTDB own bitemporal point-in-time reconstruction and supersession (kills the broad reads of (a) and (e)); VSA/HRR and Query2Box own composable geometric/symbolic operators (kills the broad read of (c)); embedding concept-drift and agent-identity-anchor work own distance-from-baseline drift (kills the broad read of (b)); and HindsightBench + Agentic Time Machine own hindsight auditing and leakage filtering (bound (a) and (d)).
Novelty lives in the specific integrated mechanisms, narrowly scoped. The two genuinely defensible ideas are: (d) substrate-level future-masking of a reconstructed, reified past self — which is the strongest, and is strengthened by HindsightBench's finding that the prompt-level approach everyone else uses fails by ~52%; and (a) constructive knowledge-state reconstruction from immutable provenance as the affirmative evidentiary basis for decision auditing, distinct from behavioral probing. The unifying, defensible thread across (a)/(d)/(c)/(e) is structural guarantee by the immutable data model — the future-mask, the tamper-evidence, and the operator-chain replayability are all properties of the append-only substrate rather than of prompts, heuristics, or model cooperation. That "guaranteed-by-construction" framing is the honest core of any priority filing. Claims (c) and (e) should be folded in as facets (auditable/replayable geometry over reconstructed slices) rather than filed as standalone claims, and (b) should be filed only if the core/periphery decomposition can be made rigorous, since the surrounding drift-detection art is dense.
Caveats for the filing team: (1) this scan covered academic/product/blog prior art via web search, not a formal patent search — several bitemporal/immutable-DB patents surfaced (e.g. US 11,935,046; US 8,812,512) and must be cleared on Google Patents/Espacenet/USPTO. (2) Claim (d)'s guarantee is that the evidence-state is T-clean; it does not by itself neutralize parametric hindsight in whatever LLM reasons over that state — scope the language accordingly. (3) Dates on several 2606–2607 arXiv preprints are very recent; confirm publication precedence relative to Engram's earliest documented conception date.