In-Memory Named Object Management

• In-Memory Named Object Management is a paradigm that represents, organizes, and accesses uniquely identified data objects in volatile memory, ensuring efficient computation and dynamic interaction.
• It is applied in distributed object stores, conversational memory systems, logic-based mappings, and reversible OOP, using tailored algorithms for CRUD operations and global uniqueness.
• Key strategies include object naming, metadata indexing, and dynamic lifecycle control using data structures like hash tables, maps, and buddy memory allocators for reversible operations.

In-memory named object management refers to the architectural, algorithmic, and interface techniques by which systems represent, organize, access, and manipulate discrete data objects—each bearing a unique identifier or name—directly in volatile memory for efficient computation, reasoning, and dynamic interaction. This paradigm finds practical application in distributed big data object stores, conversational memory management for LLM-driven agents, logic-predicate persistence, and reversible object-oriented computation. The essential properties of in-memory named object management include object-level identity, direct addressability, dynamic lifecycle control, metadata or name indexing, and mechanisms to accommodate concurrent access, reclamation, or persistence across heterogeneous application domains.

1. Core Abstractions and Object Identification

Named in-memory objects are typically units of application-level state (e.g., conversational memories, Prolog facts, data buffers, OOP instances) holding both payload and metadata fields for unique identification, status, and provenance. Object identity may be defined by text labels, UUIDs, predicate/formula keys, or composite indices.

• Conversational agents (Memory Sandbox): Each memory object is a labeled text fragment (id: UUID, text: string, source: transcript pointers, visible: boolean for prompt inclusion, timestamp: creation/last edit) with affordances for renaming, editing, and visibility filtering. Default names are auto-assigned but fully editable (Huang et al., 2023).
• Distributed object store (Plasma/ThymesisFlow): Objects are allocated by name or ObjectID (160-bit opaque value with node UUID and counter), managed via metadata struct: id, size, pointer, sealed-flag, home_node. Object naming requires global uniqueness, maintained via synchronous RPC quorums at creation and metadata propagation for lookup (Abrahamse et al., 2022).
• Object–Prolog mapping: A ground Prolog predicate instance $P(t_1,\dots,t_n)$ maps bijectively to a corresponding object instance $o$ of class $C = M(P,n)$, with $o.f_i = t_i$. A registry (hash table) links unique key $k$ to objects for O(1) lookup (Llanes, 2017).
• Reversible OOP (ROOPL++): Each dynamic object comprises header (vtable-ptr, ref-count), fields, and is named by its instance reference. Reference counting allows reversible aliasing; distinct 'construct', 'new', 'delete', 'copy', 'uncopy' operations enforce statically and dynamically scoped lifecycles (Cservenka, 2018).

2. Internal Representation and Data Structures

Object representation varies across domains but typically comprises a heap-resident buffer with metadata fields sufficient for identification, lifecycle status, provenance, and efficient in-memory access. Common structures include arrays, maps, and registries for object manager indices.

Domain	Object Key	In-Memory Structure
Conversational UI	UUID, text	Array of memory objects
Distributed Store	ObjectID	Map<ObjectID, ObjectMeta>
Prolog Mapping	Unique key	Hash table: Key $\to$ Object
Reversible OOP	Ref, type	Heap block: [meta

• No explicit embedding vectors or similarity indices are present in baseline prototypes (e.g., Memory Sandbox), though future extensions propose FAISS integration, embedding storage, and salience detection pipelines (Huang et al., 2023).
• Distributed stores maintain free-space indices (balanced trees keyed by block size) and, upon allocation, update local and peer metadata maps to provide naming and pointer consistency (Abrahamse et al., 2022).
• OOP systems (ROOPL++) store objects in heap using Buddy-Memory layout, carrying type and alias metadata in two header words, with zero-initialized fields and reference count management for safe reversible operations (Cservenka, 2018).

3. Memory Management Algorithms and CRUD Operations

Creation, retrieval, update, and deletion are the fundamental object lifecycle operations, realized via domain-specific algorithms and APIs.

• Conversational Memory Sandbox:
  • Create: Highlight transcript, add as memory object; summarized creation bundles texts and invokes LLM (GPT-3.5) to produce abstractive summaries.
  • Retrieve: Direct inclusion of visible objects via UI toggling; no semantic search.
  • Edit/Delete: Inline editing updates text and timestamp; deletion removes from array/store.
• Distributed Object Store:
  • CreateObject: Allocates buffer of requested size from node’s heap or disaggregated region using first-fit allocator; ObjectID reservation is confirmed via gRPC RPC to peers. On success, object is ‘sealed’ and becomes immutable (Abrahamse et al., 2022).
  • GetObject: Local map lookup, else remote gRPC lookup; zero-copy mapping to virtual address space.
  • ReleaseObject: Decrements local reference count; reclaims region if no longer used locally.
• Prolog–Object Mapping:
  • Create: Map $P(t_1, …, t_n)$ to object $o$ via declared mapping, assign unique key, add to in-memory registry, assertProlog($P(t_1, …, t_n)$).
  • Read: Registry lookup; on miss, reconstruct from Prolog query and cache in registry.
  • Update/Delete: Field change is persisted via retract/assert within transactions; registry updates mirror object state (Llanes, 2017).
• ROOPL++ Buddy-Memory Allocator:
  • Allocation: Finds smallest free block $\geq$ requested, splits via reversible buddy system (block sizes $S_k = 2^k S_{min}$, address buddy$(a, k) = a \oplus S_k$), sets up headers.
  • Deallocation: Checks for buddies in free list for reversible mergers; only merges head-of-list pairs. Safe only when reference count returns to one (Cservenka, 2018).
  • High-level reversible pseudocode and concrete reversible assembly instructions systematically invert all operations to prevent garbage or state loss.

4. Object Naming, Indexing, and Metadata Strategies

Object naming schemes and metadata management underpin lookup, synchronization, and lifecycle tracking.

• Global Uniqueness: Plasma/ThymesisFlow requires globally unique ObjectIDs promulgated via synchronous RPC to prevent collisions; metadata struct per store guarantees object integrity and facilitates zero-copy remote access (Abrahamse et al., 2022).
• Object Registry: Object–Prolog mapped systems use a global hash table (Key $\to$ Object), supporting reverse indexing for updates and safe concurrency extensions (read–write locking, Prolog transaction wrapping) (Llanes, 2017).
• User-Editable Labels: Memory Sandbox foregrounds user-driven naming, supporting intuitive, editable identities and drag-and-drop reference sharing; such labels become persistent symbols in the object management canvas (Huang et al., 2023).
• Buddy Memory Layout: ROOPL++ attaches vtable and reference counters to facilitate method dispatch (dynamic polymorphism), enable runtime safety checks (deletion and copy are blocked unless reference count criteria are met), and ensure reversibility (Cservenka, 2018).

5. Performance Metrics and Experimental Evaluation

Performance analysis focuses on throughput, latency, and CRUD complexity; evaluation strategies vary according to application context.

• Distributed Memory Object Store:
  • Throughput: Local access $T_{local}\approx 6.5$ GiB/s; remote $T_{remote}\approx 5.75$ GiB/s; penalty factor $P=1.13$ (11.5% slowdown for remote).
  • Latency: Local $L_{local}(N)\approx 1.885$ ms for $N=1000$ objects; remote $L_{remote}(N)\approx 5.049$ ms (dominated by gRPC setup and page mapping, $L_{grpc}\approx 2$-$3$ ms, $L_{page\_map}\sim 1$-$2$ μs/object) (Abrahamse et al., 2022).
  • No formal global in-use object tracking across stores; local stores track reference counts, and future work proposes distributed eviction coordination.
• Conversational Memory Management:
  • No quantitative evaluation; design probe only. Future metrics proposed include user satisfaction, recovery time after breakdowns, and agent recall accuracy (Huang et al., 2023).
• Object–Prolog Mapping:
  • In-memory lookups are $O(1)$ (hash table); Prolog-side operations are $O(1)$ for indexed predicates; worst-case is $O(N)$ for non-indexed cases (Llanes, 2017).
• Reversible OOP:
  • Allocation and merge complexity is $O(\log M)$; ref-count ops $O(1)$; fragmentation modeled primarily around powers-of-two block sizing (Cservenka, 2018).

6. Interactive Affordances, User Interface, and System Integrations

User interaction models differ by application, influencing affordances and operational semantics.

• Memory Sandbox UI:
  • Two-column canvas, controls for show/hide, edit, summarize, delete, share, reorder, and copy memory objects; drag-and-drop and pinch-zoom for large sets; “Add memory” and “Summarize selected” actions invoke direct LLM and UI-mediated transformations (Huang et al., 2023).
• Distributed Store Interface:
  • Object management via C/C++ API, supporting Create, Seal, Get, Release, and synchronizing operations only at commit points; gRPC extensions enable zero-copy remote pointer handoff and OpenCAPI-facilitated coherent reads, mutable only in unsealed phase (Abrahamse et al., 2022).
• Object–Prolog CRUD:
  • Host language manipulates in-memory registry, with atomic transaction facilities for safe persistence; declarative mappings allow seamless manipulation and easy extensions to concurrency and ACID properties (Llanes, 2017).
• ROOPL++/PISA:
  • All object manipulations use reversible primitives in high-level languages and assembly, preserving injectivity, safe zero-clearing, and heap structure (Cservenka, 2018).

7. Limitations, Trade-offs, and Future Directions

Limitations are contextually dependent and motivate ongoing innovations:

• Manual object retrieval and lack of semantic search: Conversational agents currently require manual inclusion/exclusion and lack embedding-based or similarity-driven search/retrieval; proposed integration with vector stores and hybrid salience detection (Huang et al., 2023).
• Distributed garbage collection: Plasma/ThymesisFlow lacks full cross-store eviction coordination and remote reference tracking; future work proposes global lease-based tracking and hybrid metadata replication to reduce lookup latency and improve safe eviction (Abrahamse et al., 2022).
• Fragmentation and reversibility trade-offs: Buddy-memory layout optimizes reversible merge/split at moderate internal overhead; object deletion and alias management are strictly controlled via header-based meta checks; harmless-garbage assumption equates free-list shapes with same blocks for subsequent injective calls (Cservenka, 2018).
• Concurrency and transactionality in logic–object mapping: Current mapping methodologies lack native concurrency but are readily extensible via host-language locking and transaction wrapping for ACID guarantees (Llanes, 2017).

A plausible implication is that ongoing integration of embedding indices, distributed metadata replication, hybrid extractive–abstractive summarization, and reversible memory systems will further optimize both transparency and efficiency in named in-memory object management across diverse computational substrates.