Tool-context compression
Smaller tool menu. Supporting diagnostic.
Across Gemini, GPT, and Claude, FieldHash cut the model's tool surface from ~28 to ~5 while function-call quality stayed in the same parity band as full-tool and prompt-smart arms. A first matched top-k run on Gemini 3.5 Flash tied FieldHash on function quality, so this remains useful context hygiene rather than a differentiated retrieval claim.
Tool surface reduction
~83%
4.74 vs 27.53 mean tools, less prompt to audit
Gold-function retention
240/240
target-tool cases retained the required function
Function-name parity
343/360
vs 339/360 full, 341/360 prompt; paired p >= 0.219
AST-like parity
335/360
vs 330/360 full, 334/360 prompt; paired p >= 0.180
False tool calls
14/360
directional only; paired p >= 0.375
For an agent, a long tool list is like a crowded control panel. The right button may be present, but every extra option adds room for confusion, review burden, and prompt load. This diagnostic shows the panel can be narrowed before the model acts; it does not show that FieldHash beats commodity top-k retrieval on benign tool selection.
The outcome: A smaller, more auditable answer path that can reduce prompt load and make agent behavior easier to review. The current evidence supports quality parity against full-context baselines, not a unique routing moat.
Tool calling fails when the relevant tool is surrounded by plausible neighbors.
Modern agents often expose the base model to a large menu of tools, schemas, and distractors. That improves capability coverage, but it also increases the chance of irrelevant calls, argument confusion, and unnecessary prompt load.
This benchmark tests a narrower question: can a governed routing layer reduce what reaches the model without hiding the function the task actually requires?
Visible tool surface
Full-tool baseline
27.53 mean tools
Prompt-smart baseline
27.53 mean tools
FieldHash filtered path
4.74 mean tools
The measured result is a much smaller tool surface, the required function retained in every target-tool case, and function-call quality held at parity against full-tool and prompt-smart arms. A Gemini 3.5 Flash matched top-k comparator tied FieldHash on function-name and AST-like quality, so differentiated efficiency is not claimed.
Results
Three provider paths, same control-plane pattern.
The run used BFCL V4 simple, multiple-function, and irrelevance task families across Gemini 3.1 Flash Lite, GPT-5.5, and Claude Opus 4.7. Each arm saw the same task rows; the difference was how much visible tool context was sent to the model.
Function-call quality
AST-like correctness: whether the selected function and structured arguments match the expected call pattern. The one-to-five row deltas below are paired-parity diagnostics, not quality-lift claims.
Full-tool baseline
330/360 AST-like
Prompt-smart baseline
334/360 AST-like
FieldHash filtered path
335/360 AST-like
False tool calls on irrelevance
Full-tool baseline
17 false calls
Prompt-smart baseline
16 false calls
FieldHash filtered path
14 false calls
Lower is better here. The effect is modest and directional only; the paired no-call comparison did not establish significant separation.
Provider replication
The aggregate is not hiding a single-provider result.
Each provider saw the same 120 BFCL-derived rows and the same three arms. Values are shown as full-tool / prompt-smart / FieldHash filtered, except visible tools, which shows full-tool to filtered.
Gemini 3.1 Flash Lite
GPT-5.5
Claude Opus 4.7
Audit checks
The compression result is checked against the raw run artifacts.
Because compression can look good by hiding necessary context, the review checks focus on whether the filtered path kept the required tool, survived a different distractor shuffle, and matches the underlying run records.
Retention is measured under distractor sets that exclude near-equivalent tools, so exact-function scoring stays unambiguous. The result tests clutter reduction without hiding the required function; it does not claim robustness to every possible tool synonym or duplicate schema.
The close function-calling differences are treated as quality parity. Exact paired McNemar checks did not show significant lift: function-name p=0.219 versus full-tool and p=0.625 versus prompt-smart; AST-like p=0.180 and p=1.000; no-call on irrelevance p=0.375 and p=0.625. A follow-up Gemini 3.5 Flash matched top-k arm tied FieldHash on function-name and AST-like totals. The core result is context-hygiene instrumentation rather than a public superiority claim.
Retention check
240/240
Every target-tool case in the three-provider aggregate kept the required gold function visible to the model.
Seed replication
80/80
A second Gemini 3.1 Flash Lite run with a different distractor seed retained every required function and preserved the compression pattern.
Paired quality check
no lift
Exact paired tests did not find significant separation from full-tool or prompt-smart arms; a Gemini 3.5 Flash matched top-k comparator tied FieldHash on function quality.
Consistency gate
18 tests
The tool benchmark test suite recomputes the public numbers from the raw run artifacts, including retention and seed-replication checks.
Context control is useful infrastructure.
The strong claim is not that symbolic manifolds beat frontier models at generation, or that this filter beats commodity top-k retrieval. The supported claim is narrower: Governed Actions can shape and audit the model's operating context before generation begins.
In practice, that can mean fewer irrelevant tools, shorter prompts, and a cleaner decision surface for the same underlying model.
How to read the result.
BFCL-derived internal distractor-pressure diagnostic, not an official BFCL leaderboard score. Distractor sets exclude near-equivalent tools to keep exact-function scoring unambiguous, so gold-function retention should not be read as a differentiated selection moat. Exact paired checks did not establish a quality lift over full-tool or prompt-smart arms. A first matched top-k comparator on Gemini 3.5 Flash tied FieldHash on function-name and AST-like quality. The result supports context-hygiene instrumentation, not a public claim that FieldHash beats commodity tool retrieval.
The result remains live as a supporting product-architecture diagnostic because it verifies context narrowing, gold-function retention, and auditability. It should not be read as a tool-retrieval superiority benchmark: the first matched top-k comparator tied FieldHash on function quality. The main tool-authority claim lives in the revoked-action diagnostic.
Read the technical context.
The technical whitepaper places this diagnostic beside Governed Memory, Governed Actions, FieldHash Ledger evidence, and the deployment posture for enterprise review.