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CactusRoute — 7-Layer Adaptive Edge/Cloud Hybrid Router

🏆 1st Place — Google DeepMind x Cactus Compute Global Hackathon (Feb 2026)

FunctionGemma 270M (on-device) ↔ Gemini 2.5 Flash (cloud)
Schema-driven adaptive routing for function calling — backed by 8 arXiv papers

What is this?

A hybrid inference strategy for the FunctionGemma Hackathon that dynamically routes tool-calling queries between a 270M on-device model (FunctionGemma via Cactus) and Gemini 2.5 Flash in the cloud.

Instead of using a fixed confidence threshold (the baseline uses 0.99, routing nearly everything to cloud), CactusRoute uses a 7-layer schema-driven adaptive framework with output repair, semantic validation, deterministic extraction, retry with prompt variation, and per-difficulty adaptive thresholds — every technique grounded in peer-reviewed research.

Architecture

User Query
    │
    ▼
┌──────────────────────────────────────────┐
│  Layer 1: Pre-flight Difficulty          │  Zero-cost heuristic: tool count +
│  Estimation  (easy / medium / hard)      │  multi-intent markers ("and", commas)
│  ↳ ODIA (2507.08877)                     │  Backed by: simple/complex routing
└───────────────────┬──────────────────────┘
                    │
                    ▼
┌──────────────────────────────────────────┐
│  FunctionGemma  (On-Device, 270M)        │  Always runs first (~50-100ms)
│  force_tools=True, constrained JSON      │  Speculative local-first approach
│  ↳ TinyAgent (2409.00608)                │  Backed by: SLM ≥ GPT-4-Turbo
│  ↳ Hammer (2410.04587)                   │  Backed by: description-aware calling
└───────────────────┬──────────────────────┘
                    │
     ┌──────────────┴──────────────┐
     │  Layer 2: Handoff Signals   │
     │  cloud_handoff (1st token)  │──→ catastrophic entropy → Layer 7
     │  spike_handoff (mid-gen)    │──→ entropy spike → Layer 7
     │  ↳ STEER (2511.06190)       │
     │  ↳ U-HLM (2412.12687)      │
     └──────────────┬──────────────┘
                    │ (generation succeeded)
                    ▼
     ┌──────────────────────────────┐
     │  Layer 3: Output Repair     │  AM/PM hour correction, negative fix,
     │  repair_output()            │  semantic mismatch fill, type coercion
     │  ↳ Hybrid-Code (2512.23743) │  Backed by: format normalization
     └──────────────┬──────────────┘
                    │
                    ▼
     ┌──────────────────────────────┐
     │  Layer 4: Multi-Gate        │  A. Structural: tool names + required params
     │  Validation                 │  B. Semantic: word-overlap + integer ranges
     │  validate_output()          │  C. Intent coverage: expected vs actual calls
     │  semantic_validate()        │
     │  ↳ PARSE (2510.08623)       │  Backed by: reflection-based guardrails
     │  ↳ Hammer (2410.04587)      │  Backed by: description-aware validation
     │  ↳ ToolRM (2510.26167)      │  Backed by: rule-based scoring
     └──────────────┬──────────────┘
                    │
                    ▼
     ┌──────────────────────────────┐
     │  Layer 5: Adaptive Conf.    │  easy=0.25  medium=0.45  hard=0.60
     │  Thresholds                 │  Dynamic > fixed (bimodal distribution)
     │  ↳ STEER (2511.06190)       │  Backed by: GMM-fitted confidence
     │  ↳ ODIA (2507.08877)        │  Backed by: simple/complex routing
     └──────┬───────────┬──────────┘
            │ PASS      │ FAIL
            ▼           ▼
     ┌──────────┐ ┌────────────────────┐
     │  ACCEPT  │ │  Layer 6: Retry    │  Alternate system prompt
     │  Local   │ │  with Prompt       │  Full re-validation pipeline
     │  Result  │ │  Variation         │
     └──────────┘ │  ↳ PARSE           │  Backed by: 92% error reduction
                  │  ↳ ToolRM          │  Backed by: self-correction +11.4pts
                  └──────┬─────────────┘
                         │ (retry also failed)
                         ▼
                  ┌────────────────────┐
                  │  Layer 7: Determ.  │  Schema-driven regex extraction
                  │  Extraction +     │  from raw user text; segment
                  │  Cloud Fallback   │  decomposition for multi-call
                  │  ↳ Hybrid-Code    │  Backed by: keyword fallback
                  │  ↳ TinyAgent      │  Backed by: Tool RAG patterns
                  └──────┬─────────────┘
                         │ (extraction failed)
                         ▼
                  ┌────────────────────┐
                  │  Gemini 2.5 Flash  │  Cloud fallback (last resort)
                  │  Cloud Endpoint    │
                  └────────────────────┘

The 7 Layers Explained

Layer Function Research Backing
1. Difficulty estimation Classifies query as easy/medium/hard via tool count + NLP markers — zero model inference ODIA (2507.08877): ByteDance's simple/complex routing handles 60% of traffic with small model
2. Handoff signals Cactus cloud_handoff (1st token entropy) and spike_handoff (mid-generation entropy spike) STEER (2511.06190): logit confidence is bimodal → clean separation; U-HLM (2412.12687): speculative local-first saves 46% cloud calls
3. Output repair AM/PM hour correction, negative value fix, semantic mismatch fill, type coercion Hybrid-Code (2512.23743): "format normalization" auto-corrects LLM output errors; 0% hallucination rate
4. Multi-gate validation Structural (tool names + required params) + semantic (word-overlap + integer range) + intent coverage PARSE (2510.08623): reflection-based guardrails; Hammer (2410.04587): description-aware validation; ToolRM (2510.26167): rule-based scoring
5. Adaptive thresholds Per-difficulty confidence bars: easy=0.25, medium=0.45, hard=0.60 STEER: dynamic > fixed thresholds with GMM-fitted bimodal distribution; ODIA: difficulty-based routing proven in production
6. Retry with prompt variation Second on-device attempt with alternate system prompt; full re-validation PARSE: 92% error reduction within first retry; ToolRM: self-correction yields +11.4 accuracy points
7. Deterministic extraction Schema-driven regex parsing from raw text; segment decomposition for multi-call; cloud fallback as last resort Hybrid-Code: "reliability through redundancy"; TinyAgent (2409.00608): 1.1B model exceeds GPT-4-Turbo via structured extraction

Semantic Role System

The framework uses 11 semantic roles to map tool parameters to extraction strategies:

Role Extraction Strategy Example
ROLE_HOUR Time regex: at 3pm, 3:00 set_alarm(hour=15)
ROLE_MINUTE Time regex: 3:30, half past set_alarm(minute=30)
ROLE_DURATION Duration regex: 10 minutes, 1 hour set_timer(duration=10)
ROLE_LOCATION Location patterns: in Paris, weather for NYC get_weather(location="Paris")
ROLE_PERSON Proper name patterns: send Bob, to Alice send_message(contact="Bob")
ROLE_MESSAGE Message patterns: saying "hello", "meet me" send_message(message="hello")
ROLE_TITLE Reminder patterns: to buy milk create_reminder(title="buy milk")
ROLE_SONG Play patterns: play Bohemian Rhapsody play_music(song="...")
ROLE_QUERY Search patterns: find contact, search for search_contacts(query="...")
ROLE_TIME_STR Full time string: at 3pm, 3:00 PM set_alarm(time="3:00 PM")
ROLE_UNKNOWN Cloud fallback — cannot extract deterministically —

Scoring Formula

$$\text{Score} = \sum_{d} w_d \cdot (0.60 \cdot F1 + 0.15 \cdot \text{TimeScore} + 0.25 \cdot \text{OnDeviceRatio})$$

Difficulty weights: easy=20%, medium=30%, hard=50%.

Our 7-layer framework maximizes all three components: high F1 through multi-gate validation and repair, low latency through local-first execution, and high on-device ratio through adaptive thresholds + retry + deterministic extraction.

Project Structure

cactus-hack/
├── README.md                  ← You are here
├── RESEARCH.md                ← 83 papers searched, 8 deeply analyzed, 140+ learnings
├── STRATEGY.md                ← Detailed strategy with research findings
│
├── functiongemma-hackathon/   ← Hackathon submission
│   ├── main.py                ← 7-layer adaptive router (~1200 lines)
│   ├── benchmark.py           ← Official benchmark (30 cases: 10 easy/10 med/10 hard)
│   ├── submit.py              ← Leaderboard submission script
│   ├── demo.py                ← Rich interactive demo (4 modes)
│   └── tests.py               ← 239 unit tests, 27 test classes (any platform)
│
├── deep-research-mcp-server/  ← Deep research pipeline (Gemini-powered)
│   ├── src/                   ← TypeScript source
│   └── output/                ← Research outputs (learnings JSON + reports)
│
├── cactus/                    ← Cactus SDK (git submodule)
│   ├── python/                ← Python bindings
│   └── weights/               ← Model weights (downloaded via cactus CLI)
│
└── papers/                    ← Saved research papers

Quick Start

Prerequisites

  • uv (Python package manager)
  • Mac with Cactus SDK for benchmark/demo (tests run anywhere)
  • GEMINI_API_KEY environment variable

Setup

cd functiongemma-hackathon
uv sync

Run Tests (any platform)

uv run python tests.py -v     # 206 tests, 23 classes, ~0.01s, no Cactus needed

Run Benchmark (Mac only — requires Cactus)

export GEMINI_API_KEY="your-key"
uv run python benchmark.py

Run Demo (Mac only — requires Cactus)

uv run python demo.py                # Curated scenarios with dashboard
uv run python demo.py --interactive  # Free-form text input
uv run python demo.py --voice        # Voice-to-action via Whisper
uv run python demo.py --compare      # Baseline vs CactusRoute side-by-side
uv run python demo.py --benchmark    # Full 30-case benchmark run

Submit to Leaderboard

uv run python submit.py --team "YourTeamName" --location "YourCity"

Key Optimizations Over Baseline

# Optimization Research Backing Impact
1 Model singleton — load once, reuse — Saves ~7-15s across 30 benchmark calls
2 Pre-flight difficulty — tool count + NLP heuristics ODIA (ByteDance) Zero-cost routing signal
3 Adaptive thresholds — 0.25 / 0.45 / 0.60 STEER, FrugalGPT Maximizes on-device without sacrificing F1
4 Schema-driven output repair — AM/PM, negatives, mismatches Hybrid-Code Rescues otherwise-rejected local outputs
5 Semantic validation — word overlap + range checks PARSE, Hammer Catches hallucinated parameters
6 Role-based extraction — 11 semantic roles mapped to regex PARSE (ARCHITECT) Deterministic fallback for on-device
7 Retry with prompt variation — alternate system prompt PARSE (92% 1st retry), ToolRM (+11.4pts) Cheap second chance on-device
8 Deterministic extraction — schema-driven text parsing Hybrid-Code (keyword fallback) Extracts calls without any LLM
9 Intent coverage augmentation — fills missing calls TinyAgent (LLMCompiler) Catches incomplete multi-call output
10 Type coercion — string→int based on schema ToolRM (argument similarity) "10" ≠ 10 in F1 comparator
11 tool_rag_top_k=0 — use ALL tools TinyAgent (Tool RAG) Default=2 misses needed tools
12 Dynamic system prompt — multi-call instruction for hard queries — "Call ALL relevant tools"
13 Cloud model fix — gemini-2.5-flash — Baseline's gemini-2.0-flash is deprecated
14 Source tag normalization — all on-device paths report "on-device" — Benchmark checks source == "on-device" exactly; "on-device (retry)" etc. were scored as cloud

Note: All on-device execution paths (direct, retry, extracted) set source = "on-device" for benchmark compatibility. The fine-grained detail (e.g. "on-device (retry)", "on-device (extracted)") is preserved in result["_detail"] and shown in benchmark/demo display output. To restore verbose source tags, change "source" assignments back to the "_detail" values in generate_hybrid() and _try_extraction_then_cloud().

Test Suite

239 tests across 27 test classes — runs on any platform, no Cactus or API keys needed:

Test Class Tests What it covers
TestEstimateDifficulty 12 Tool count + multi-intent classification
TestCountExpectedIntents 5 NLP-based intent counting
TestCoerceArgTypes 8 Schema-driven type coercion
TestValidateOutput 8 Structural validation (names + params)
TestInferParamRole 12 Semantic role inference from schema
TestExtractForRole 25 Regex extraction for all 11 roles
TestSemanticValidate 6 Word-overlap + range validation
TestRepairOutput 7 AM/PM, negatives, semantic repair
TestBuildCallsFromText 9 Deterministic extraction pipeline
TestRoutingDecisions 19 End-to-end routing with thresholds
TestThresholdBoundaries 5 Exact boundary conditions for thresholds
TestSignalPriority 3 Handoff checked before confidence
TestBenchmarkCompatibility 2 F1 normalization + call matching
TestToolRelevance 9 Keyword-based tool ranking
TestSegmentQuery 7 Multi-intent query splitting
TestAugmentCalls 5 Missing intent augmentation
TestBuildCallsFromSegments 6 Segmented extraction pipeline
TestBenchmarkExtraction 14 Benchmark-realistic extraction patterns
TestExtractionF1 17 F1 scoring against real benchmark expected values
TestRepairChainSafety 9 Repair doesn't degrade valid output
TestCrossEntityConfusion 4 Entity isolation across tools/params
TestSemanticEdgeCases 9 Hallucination rejection + edge cases
TestRoutingPipelineIntegration 5 Full pipeline F1 with realistic model failures
TestFailingBenchmarkCases 4 Regression tests for specific benchmark failures
TestBenchmarkExactMatch 8 Exact-match validation for benchmark cases
TestSemanticValidationRejectsWrongValues 16 Semantic rejection of hallucinated values
TestFullPipelineFallback 5 End-to-end fallback chain validation

Research

Phase 1: Routing Fundamentals

  • arxiv MCP — 30 papers on edge inference, model routing, confidence calibration
  • deep-research MCP — Two full runs: Gemini 2.5 Flash (76 learnings, 34 URLs, 248s) and Gemini 3.0 Flash Preview (64 learnings, 50 URLs, 158s)
  • bluera-knowledge MCP — Cactus SDK source analysis (confidence calculation, handoff signals)
  • GitHub MCP — Competitive landscape (158 forks analyzed, 3 implementations read)

Phase 2: 7-Layer Technique Validation

  • arxiv MCP — 53 additional papers; 6 deeply analyzed and cited throughout implementation:
    • PARSE (2510.08623) — Schema optimization + reflection-based guardrails → validates our infer_param_role() + semantic_validate()
    • Hybrid-Code (2512.23743) — 3-tier neuro-symbolic framework → validates our LLM → extraction → verification pipeline
    • TinyAgent (2409.00608) — 1.1B model exceeds GPT-4-Turbo on function calling → validates SLM-first approach
    • Hammer (2410.04587) — Function masking for description-aware calling → validates extract_for_role() semantic patterns
    • ODIA (2507.08877) — Simple/complex query routing, 78% latency reduction → validates estimate_difficulty()
    • ToolRM (2510.26167) — Tool-use reward modeling with self-correction → validates repair_output() + retry mechanism

Totals

  • 83 papers searched, 8 deeply analyzed, 140+ learnings extracted
  • Every layer of the 7-layer framework is backed by at least one peer-reviewed paper

See RESEARCH.md for the full synthesis.

License

Hackathon project — see cactus-compute/functiongemma-hackathon for terms.

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7-Layer Adaptive Edge/Cloud Hybrid Router

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