| name | 0.4.4-understand-working_memory |
| description | [0.4.4] The transient token budget allocated for active reasoning within a single model call; scratch space for chain_ |
understand-working_memory
CALL NUMBER: llm_memory_and_context_engineering.working_memory : deep_context_window(13)
DEFINITION: The transient token budget allocated for active reasoning within a single model call; scratch space for chain_of_thought and intermediate results
Invoke this skill to understand working_memory down to its primitives. The RELATIVE ROOT below is the least-fixed-point closure of everything it bundles from — the full import cone, grouped by the lib each prim comes from. Projected from a prover-typed KB (MAP/SWI-Prolog consistency gate): every reference below resolves.
THE RELATIVE ROOT (the import cone, by lib)
from deep_context_window
- context_boundary (d3): The hard edge position within context where context_overflow triggers if exceeded; exactly context_length minus one for the output region.
- context_resize_event (d3): The runtime mechanism or policy invoked when context_length must accommodate inputs exceeding current capacity; involves reallocation or overflow handling.
- context_scaling_factor (d3): The multiplier relating raw context_length to effective_context; models with improved attention patterns achieve factors closer to one.
- input_token_budget (d3): The portion of context_length reserved and available for prompt tokens, retrieved context, and system instructions before generation begins.
- max_model_tokens (d3): The absolute token ceiling of the model architecture; synonymous with context_length when no additional constraints apply.
- maximum_position (d3): The highest valid integer position index representable by the position_embedding scheme within context_length; equal to max_model_tokens minus one for zero-indexed sequences.
- output_token_limit (d3): The portion of context_length reserved for generated tokens; equals context_length minus input_token_budget minus overhead tokens.
- overflow_threshold (d3): The position or token count at which the model behavior shifts from normal processing to truncation or error; often slightly below context_boundary.
- token_boundary_marker (d3): The positional delimiter marking the transition from input context to output generation; tokens before this marker are provided context, tokens after are to be generated.
- context_capacity_ratio (d4): The ratio of tokens actively processed to total available context_length; quantifies context_utilization as used_capacity divided by max_model_tokens.
- token_headroom (d4): The arithmetic difference between current position or token count and context_boundary; the remaining room before overflow_threshold is reached.
- position_index (d4): Ordinal integer from zero to context_window_boundary representing each token's location in the sequence; each index must be encodable by the position_embedding scheme in use
- sequence_position (d4): The ordinal index of a token within the full context sequence; constrained to be less than or equal to maximum_position.
from llm_memory_and_context_engineering
- attention_mechanism (d1): The core operation allowing each token to attend to all others; scaled dot-product attention is the standard. Memory and context are both outputs of attention computation.
- chain_of_thought (d1): Producing intermediate reasoning steps before final answer; when demonstrated in context, subsequent reasoning follows the pattern. A form of contextual procedural memory.
- context_window (d1): The finite token sequence an LLM can process in a single forward pass; defines the maximum input + output capacity bounded by architecture or inference config
- memory_efficiency (d1): Ratio of useful information retained per unit of memory storage or context_token budget
- memory_hierarchy (d1): The layered structure of LLM memory: immediate context (working), retrieved context (RAG), fine-tuned weights (parametric), external stores (vector DB). Each layer has different latency/capacity tradeoffs.
- memory_window (d1): The subset of episodic_memory available for retrieval at any time; analogous to context_window but for long_term_storage
- scratchpad (d1): Explicit intermediate reasoning space written by the model itself; a form of ephemeral working_memory externalized to tokens
- sliding_window (d1): Fixed-size contiguous subset of recent tokens kept in working_memory as new tokens arrive and old ones are evicted
- attention_saturation (d2): Condition where attention weights become uniform across long context; signals reduced context_utilization and effective_context shrinkage
- attention_sinks (d2): Special tokens that accumulate disproportionate attention mass, enabling efficient long-context modeling without explicit positional encoding
- attention_weighting (d2): Distribution of attention scores across context tokens; reveals what the model considers relevant to current generation
- cross_attention (d2): Attention mechanism where queries attend to a separate context (e.g., retrieved documents); enables retrieval_augmented_generation
- hard_attention (d2): Attention focusing on single positions; non-differentiable but efficient; approximated by soft attention in practice
- kv_cache (d2): The key-value tensors cached during transformer self-attention to avoid recomputing activations for previously-seen tokens
- self_attention (d2): The mechanism by which transformer models relate each token position to all other positions; the quadratic bottleneck in context_length scaling
- soft_attention (d2): Attention producing weighted averages over all positions; differentiable but computationally expensive at scale
CONSUMERS (what needs this)
chain_of_thought, context_window, kv_cache, sliding_window
Projected from the llm memory and context engineering KB (438 concepts / 213 relations) — consistency-typed by MAP; the facet list after the colon IS the cross-lib dependency web.
(leaf — this is an actual skill.)
