Projects / Machines With Human-Like Memory
Subproject
RoomKG Baselines
The RoomKG benchmark and baseline implementations accompanying Temporal Knowledge-Graph Memory in a Partially Observable Environment.
Benchmark
Reframe the memory problem in temporal knowledge-graph terms.
Figure 1
Temporal knowledge-graph framing
- Temporal knowledge-graph memory
- Partial observability retained
- Explicit benchmark framing
- Shared evaluation surface
RoomKG Baselines turns the earlier room-memory benchmark into a more explicit graph-memory setting. Observations, hidden state, and memory can now be represented and studied as temporal knowledge graphs rather than only as symbolic records or latent policy state.
That gives the research line a stronger benchmark object for comparing graph-based memory approaches under partial observability.
Baselines
Compare symbolic and neural approaches under one setup.
Figure 2
Baseline comparison layer
- Symbolic baselines
- Neural baselines
- Shared evaluation protocol
- Reusable benchmark artifacts
The repository collects baseline implementations so symbolic, neural, and hybrid approaches can be evaluated under the same benchmark conditions. That makes the work more than a paper artifact: it is the comparative layer around the benchmark itself.
Instead of burying the comparison inside one system, the project makes the baseline surface explicit and reusable.
Direction
Push the broader project toward graph-based long-term memory.
Figure 3
Later-phase research direction
- Later stage in the project line
- Graph-structured memory focus
- Benchmark plus baselines
- Foundation for follow-on work
RoomKG Baselines marks a later phase in Machines With Human-Like Memory, where the emphasis shifts from explicit memory systems in general to temporal knowledge-graph memory in particular.
It is the point where the benchmark, the implementation surface, and the research question all align around graph-structured memory.
Resources