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TMAS: Scaling Test-Time Compute with Multi-Agent Synergy
A visual explainer of how TMAS tries to turn extra inference compute into coordinated reasoning:
specialized proposal, verification, refinement, and memory agents exchange reusable intermediate state so later attempts get sharper instead of merely longer.
Episode Focus
Managed test-time scaling, hierarchical shared memory, compute-matched evaluation, and the difference between real multi-agent synergy and costly redundant parallelism.
TMAS is best read as a controller around a base model. The key bet is that useful fragments from one reasoning wave can be compressed, filtered, and fed into the next wave without collapsing diversity.
Multi-Agent Pipeline
proposalverificationrefinementmemory banks
Contrast with self-consistency
Shared state
Not just many samples. Later trajectories consume filtered residues from earlier ones.
Central risk
Noise ratification
If memory admits bad clues or strategy bias, coordination makes all agents more consistently wrong.
What makes the claim interesting
Compute reuse
Intermediate facts, local checks, and higher-level guidance are retained instead of repeatedly rediscovered.
Hierarchical Memory
TMAS separates low-level experience from higher-level guidance. The interaction below shows which problem regions trigger reuse, exploration pressure, or conflict between memory and fresh search.
RoundsR3
Experience vs Guideline Activation Map
cold / unusedreuseddominant / risky
Memory Bank Composition
The lower bank stores local evidence and verified fragments; the upper bank stores strategy guidance. Hover cells to inspect what later trajectories inherit.
Compute-Matched Results
The paper’s strongest argument depends on budget honesty. These mock plots visualize the core audit question: does extra compute produce independent gains, or mostly recirculate tokens and verifier overhead?
Accuracy vs Total Test-Time Budget
Where the Tokens Go
Mock token accounting includes proposals, verifiers, refinement loops, and memory re-consumption. The useful comparison is equal total budget, not equal loop count.
Method Landscape
TMAS sits between simple sample diversity and fully coordinated search. This map positions nearby methods by coordination intensity and degree of explicit memory reuse.
Coordination vs Reuse Map
Ablation Pressure Test
The cleanest skeptic’s question: under matched calls, tokens, and wall clock, how much survives if role specialization or one memory bank disappears?