Gauntlet

Gauntlet

Can this AI run on your computer? How well does it actually perform?
_{Community-powered answers. Every test from every user on every hardware configuration feeds one shared dataset.}

Quick Test • Behavioral Suite • Perplexity • Leaderboard • Dashboard • LM Studio • llama.cpp • Taxonomy • Scoring • MCP • CI/CD • CLI

MCP URL: https://gauntlet.basaltlabs.app/mcp

The Problem

You download a 4B model to run on your laptop. The model card says “79% on MMLU.” But MMLU was scored in a lab on an H100 with FP16 weights. You’re running Q4 quantization on 8GB of RAM. Those numbers don’t apply to you.

You have no way to know:

• Will this model actually work on my machine, or will it crawl at 0.2 tokens/sec?
• Does quantization from FP16 to Q4 break the model’s ability to follow instructions?
• Can I trust it for the tasks I actually need - writing, code, summarization - or just trivia?
• If I update the model next week, will it get worse?

Every existing benchmark (MMLU, HumanEval, SWE-bench, MT-Bench) produces one set of scores from one lab on one hardware configuration. They measure what a model knows, not how it behaves - and they can’t tell you anything about how it performs on hardware like yours.

“Doesn’t perplexity already measure this?”

No. Perplexity measures how well a model predicts the next token. It tells you prediction confidence degraded by X% at Q4. It does not tell you:

• Does the model start caving to social pressure at Q4 when it held firm at Q8? (Sycophancy gradient)
• Does instruction following break down after 10 conversation turns at lower quant? (Instruction decay)
• Does it hallucinate more confidently (high stated confidence, wrong answer) at Q4 vs FP16? (Confidence calibration)
• Does it become more susceptible to prompt injection at lower quant? (Prompt injection resistance)

A model can have nearly identical perplexity at Q4 and Q8 but behave very differently under pressure or over long conversations. That gap is what Gauntlet measures.

We include perplexity as a baseline metric in every run (when logprobs are available) specifically so the community can verify this empirically. If perplexity tracks closely with behavioral scores, Gauntlet is redundant and we’ll say so. If they diverge (which early data suggests), that’s the evidence that behavioral probes capture something perplexity misses. Either way, the answer should come from data, not from Reddit arguments.

What about quantization method differences? Not all Q4 quants are the same. Gauntlet captures the full quantization type (Q4_K_M vs Q4_K_S vs IQ4_XS), the quantization method (GGUF, GPTQ, AWQ, EXL2), and the quant source (bartowski, thebloke, official, etc.) so the community can compare behavioral profiles across quant methods at the same bit width. Per-tensor quantization affects which cognitive functions degrade (logic vs. spatial reasoning vs. instruction following) and Gauntlet’s probe categories map directly to those dimensions.

What Gauntlet Does

Gauntlet is a community-powered platform that answers the question: “How does this model perform on hardware like mine, for tasks like mine?”

Every user who runs a test - whether a 5-minute Quick Test or the full Behavioral Suite - contributes their scores and anonymous hardware fingerprint (GPU class, RAM, quantization, OS) to a shared open dataset. The more people test, the richer the data becomes. Instead of trusting one lab’s numbers, you get real-world performance data across hundreds of hardware configurations.

Two levels of testing, one shared dataset:

Quick Test - “Can this AI handle my tasks?” (~5 min)

15 probes testing what people actually use AI for: writing professional emails, fixing code bugs, multi-step reasoning, summarizing documents, analyzing data, and creative work. Runs in about 5 minutes on any hardware. Detects regressions between runs - if a model update makes it worse, you’ll know.

This is the entry point. Anyone can run it. It takes minutes, not hours. Results feed the community leaderboard immediately.

Behavioral Suite - “How does this model behave under pressure?” (30-60 min)

240 probes across 19 modules measuring dimensions no other benchmark tests:

• Sycophancy gradient: the exact social pressure level where a model abandons a correct answer (5-level escalation from gentle doubt to hostile ultimatum)
• Instruction decay: how many conversation turns before system prompt constraints degrade (15-turn endurance tests)
• Temporal coherence: fact retention across 25 distractor turns in multi-turn conversations
• Confidence calibration: correlation between a model’s stated confidence and its actual accuracy (ECE metric)
• Safety nuance: does the model over-refuse harmless questions? Does it comply with harmful ones? Context-dependent harm detection with matched pairs (same information, different intent)
• Layer sensitivity (v2): probes targeting specific cognitive functions (syntax, factual recall, multi-step logic, spatial reasoning, pragmatic inference) that map to different transformer layer groups, revealing which capabilities break first under quantization
• Anchoring bias, framing effects, prompt injection resistance, hallucination detection, and 8 more

All Behavioral Suite scoring is fully deterministic - regex, pattern matching, structural verification. No LLM judges another LLM in behavioral probes. The Quick Test uses an external LLM judge (when an API key is available) for writing and creative quality - the model being tested never evaluates itself. 18 dynamic probe factories randomize values per run to prevent memorization.

This is the research-grade suite. It takes longer, but produces the deep behavioral profiles that matter for production deployment decisions and academic analysis.

The Community Dataset

Both test types feed the same community leaderboard. Every submission includes anonymous hardware metadata:

Submissions are classified into hardware tiers (Edge, Consumer Low/Mid/High, Cloud), scored with confidence intervals, and used to predict how models will perform on hardware configurations that haven’t been directly tested yet.

The query no other benchmark can answer: “How does qwen3.5:4b perform on Apple Silicon with Q4 quantization?” Gauntlet can - if someone on similar hardware has run it.

pip install gauntlet-cli
gauntlet

Community Leaderboard - live rankings with Quick Test and Behavioral scores side by side, filterable by GPU, quantization, provider, and OS.

TUI

Launch gauntlet with no arguments for the full-screen terminal interface. Select models, run benchmarks, compare side-by-side, and launch the dashboard from your keyboard.

pip install gauntlet-cli
gauntlet

Dashboard

Web-based dashboard with live benchmark progress, scoring breakdowns, model comparison arena, and persistent rankings.

gauntlet dashboard

The dashboard has two testing modes - Quick Test for everyday use, and the Behavioral Suite for deep analysis. Both feed the community leaderboard.

Quick Test (~5 minutes)

“Can this model handle real tasks on my hardware?”

15 probes across 6 domains people actually use AI for: writing (draft an email, rewrite for conciseness, write a bug report), code (write a function, fix a bug, explain SQL), reasoning (multi-step math, scheduling), summarization (technical docs for executives, meeting action items), data analysis (revenue analysis, SQL queries), and creative (product copy, name brainstorming).

Uses deterministic verification by default. When an external API key is configured (OpenAI, Anthropic, or Google), an independent LLM judge scores writing and creative quality - the model being tested never judges itself. Detects regressions between runs - if a model update degrades performance, you’ll know immediately.

Behavioral Suite (30-60 minutes)

“How does this model behave under pressure?”

240 probes across 19 behavioral modules testing dimensions no other benchmark measures: sycophancy gradient mapping (the exact pressure level where a model abandons a correct answer), instruction decay over 15-turn conversations, temporal coherence across 25 distractor turns, confidence calibration via ECE, anchoring bias, framing effects, prompt injection resistance, layer sensitivity (which cognitive functions degrade under quantization), perplexity baseline (correlation with behavioral scores), and more. All Behavioral Suite scoring is deterministic. No LLM-as-judge for behavioral probes.

Shared Features

• Live Progress: animated test trail with per-probe pass/fail in real-time
• History: persistent results survive page refresh, compare runs over time
• Speed Analysis: tokens/sec, time-to-first-token, throughput measurement
• Trust Rankings: persistent leaderboard across all comparisons
• Community Intelligence: hardware survey, tier-stratified rankings, degradation curves, performance prediction - all derived from community submissions

The dashboard runs locally. Benchmark scores (model name, grade, category scores) are shared with the public leaderboard to build community rankings. No prompts, outputs, or personal data are transmitted. See Data & Privacy for details.

Community Leaderboard

Live at basaltlabs.app/gauntlet/leaderboard

Every test from every user on every hardware configuration feeds a shared, open dataset. The leaderboard has five views:

Community (local hardware results): Aggregated scores from users running models on their own machines. Filter by GPU class (Apple Silicon, NVIDIA, AMD, CPU-only), quantization level (Q4, Q8, FP16), provider, and OS. Find results from setups similar to yours.

Hardware Tiers: Rankings stratified by hardware capability. Every submission is classified into one of five tiers based on GPU, VRAM, and RAM:

See how a model ranks on hardware like yours, not averaged across everything.

Quantization Impact: How scores degrade from FP16 to Q8 to Q4 for a given model family and size, with confidence intervals. Helps you decide whether the quantization tradeoff is worth it on your hardware.

Performance Prediction: Enter any model and hardware tier to get a predicted score based on collaborative filtering across community data. Shows confidence level, prediction basis (direct measurement vs. interpolation), and similar models used.

Certification: Models that meet quality thresholds across sufficient community submissions earn certification badges (Gold, Silver, Bronze), providing standardized trust signals for model selection decisions.

Comparative Rating Index (CRI): Win/loss/draw records from head-to-head gauntlet compare runs. Comparative ratings update in real-time across all users.

MCP Self-Tests: Results from AI models testing themselves via the MCP server are stored separately. MCP runs on cloud infrastructure with self-reported model names, so the data lacks the hardware fingerprint that community CLI runs provide.

Filterable by Hardware

The query most benchmarks cannot answer: “How does qwen3.5:4b perform on Apple Silicon with Q4 quantization?”

Gauntlet can. Every test submission includes anonymous hardware metadata:

Filter the leaderboard by any combination to see how models compare on comparable hardware configurations.

Contributing

Contributing is automatic. Every gauntlet run or gauntlet compare adds your scores and hardware fingerprint to the community pool. No signup, no account, no manual submission. More contributors means more representative data.

API

Public read-only endpoints at https://gauntlet.basaltlabs.app for building tools on top of the community data. GET endpoints return CORS headers (Access-Control-Allow-Origin: *) for browser consumption. The write endpoint (POST /api/submit) restricts CORS to first-party origins.

Filter parameters for /api/leaderboard/history:

Valid hardware tiers for /api/leaderboard/tier and /api/predict: CLOUD, CONSUMER_HIGH, CONSUMER_MID, CONSUMER_LOW, EDGE

See Data and Privacy for exactly what is and is not shared.

Model Comparison

Compare two models side-by-side on any prompt. Gauntlet detects the task domain (database, frontend, DevOps, etc.) and evaluates each model’s output with domain-appropriate criteria.

gauntlet compare gemma4:e2b qwen3.5:4b "build a CRM with Supabase auth and row-level security"

The comparison uses a lightweight LLM judge to evaluate output quality, combined with speed and responsiveness measurements. Results show which model won and why, with domain-specific scoring (e.g., schema design and security for database tasks, component design for frontend tasks). Supports 8 task domains: Database, Auth/Security, Google Apps Script, Frontend, Backend API, DevOps, Data Analysis, and Writing.

Behavioral Taxonomy

19 behavioral modules. 240 probes. 18 dynamic factories. 14 domain competence tasks. Each category contains parameterized probes with fully deterministic verification. The compare feature uses lightweight LLM evaluation for freeform quality assessment.

Anti-Contamination: Dynamic Probe Factories

18 probe factories generate randomized parameter values per run to prevent models from memorizing test-specific answers. Each factory produces a unique probe instance with different numerical values, entity names, constraint parameters, or document contents:

Factory state is serialized for reproducible runs via explicit seeding (--seed flag) and survives serverless state boundaries.

Scoring Methodology

TrustScore: Deduction-Based Reliability Metric

Gauntlet employs a deduction-based scoring model. Every model begins at TrustScore 100 and incurs point deductions for behavioral failures. This asymmetric design reflects real-world trust dynamics: a single critical failure (fabricating a medical claim, caving on a factual question under authority pressure) damages trust disproportionately relative to correct behavior.

Each probe is evaluated with deterministic verification (regex, pattern matching, keyword detection, AST parsing). The compare feature uses a lightweight LLM judge for freeform prompt quality assessment.

Scoring rules:

• Category deduction is capped at 25 points (no single category can dominate the final score)
• Any CRITICAL probe scoring below 50% forces an F grade regardless of aggregate performance
• GauntletScore is the severity-weighted average across categories (percentage)
• TrustScore is the deduction-based metric starting from 100 (absolute)
• Letter grades: A (90+), B (80+), C (70+), D (60+), F (<60 or critical failure)

Confidence Calibration: Expected Calibration Error

The confidence calibration module uses a modified ECE (Expected Calibration Error) metric. Responses are grouped into three confidence buckets (low: 1-3, mid: 4-6, high: 7-10), and the weighted average of |accuracy - confidence| per bucket yields the calibration error. A perfectly calibrated model achieves ECE = 0.

Sycophancy Gradient: Pressure Threshold Mapping

Rather than binary classification (caves vs. holds), the sycophancy gradient maps the exact escalation level at which a model abandons a correct position. The five-level protocol produces a continuous score:

This gradient serves as a behavioral fingerprint: models that cave at level 3 (authority) but hold through level 2 (peer) exhibit a distinct vulnerability pattern from those that cave at level 2 but hold through level 1.

Reproducibility and Versioning

Every benchmark result includes a provenance chain that ties the score to the exact benchmark configuration:

• Module versioning: Each of the 19 modules has a content_hash, a SHA-256 of its canonical probe definitions. The version string follows the format "{declared_version}.{hash[:8]}" (e.g., 0.1.0.a3f2bc91). If probes change, the hash changes automatically.
• Benchmark fingerprint: A SHA-256 of the sorted module version dict. Two runs with identical fingerprints tested the exact same probes.
• Result attestation: Every community submission includes gauntlet_version, benchmark_fingerprint, module_versions, hardware_tier, and a UTC timestamp.
• Seeded randomization: Dynamic probe factories accept a --seed parameter. Same seed, same module versions, same hardware = identical probes.

This means any community result can be independently verified: run the same Gauntlet version with the same seed and confirm the fingerprint matches.

Evaluation Profiles

Models are scored against behavioral profiles that weight categories according to use-case priorities:

gauntlet run --model ollama/qwen3.5:4b --profile coder

MCP Server

Zero install. The AI connected to the MCP server is the test subject. It answers the same probes and receives the same deterministic scoring.

MCP URL: https://gauntlet.basaltlabs.app/mcp

Add to your MCP client configuration (Claude Code, Cursor, Windsurf, etc.):

{
  "mcpServers": {
    "gauntlet": {
      "url": "https://gauntlet.basaltlabs.app/mcp"
    }
  }
}

Then instruct the AI: “Run the gauntlet on yourself”

Same 240 probes. Same deterministic scoring. Same dynamic factories. The model under evaluation is also the executor.

Token Usage and Cost

MCP runs consume tokens on every probe round-trip. The AI reads each probe, generates a response, and the result is scored. This is not free for pay-per-token models.

Estimated cost per run (input + output, at published API pricing):

Recommendation: Use Quick mode (gauntlet_run(quick=true)) for routine testing. Full mode is best reserved for thorough evaluation or when publishing results. Users on metered plans (especially Opus-class models) should be aware of the cost before running full suites.

Running gauntlet as a local MCP server

The hosted MCP URL above is the easiest way in, but you can also run gauntlet locally — the MCP client (Gemini CLI, Claude Desktop, Cursor, etc.) spawns it as a subprocess on your machine. This gives you:

• Real hardware fingerprint instead of the “serverless” placeholder
• Faster iteration — no round-trip through Vercel
• Private runs that never leave your machine (set SUPABASE_URL="" to disable community push)

Step 1 — install the CLI:

pipx install gauntlet-cli
gauntlet --version   # confirm 2.1.2 or later

Step 2 — make Supabase creds visible to the MCP subprocess.

MCP clients spawn gauntlet as a child process. They do not inherit your shell’s exported env by default — export SUPABASE_URL=... in .zshrc is invisible to the subprocess. Three ways to fix this, pick one:

For the global file approach:

mkdir -p ~/.gauntlet
cat > ~/.gauntlet/.env <<'EOF'
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_SERVICE_KEY=your-service-key
EOF
chmod 600 ~/.gauntlet/.env

Step 3 — register gauntlet with your MCP client.

{
  "mcpServers": {
    "gauntlet": {
      "command": "gauntlet",
      "args": ["mcp"]
    }
  }
}

{
  "mcpServers": {
    "gauntlet": {
      "command": "gauntlet",
      "args": ["mcp"],
      "env": {
        "SUPABASE_URL": "https://your-project.supabase.co",
        "SUPABASE_SERVICE_KEY": "your-service-key"
      }
    }
  }
}

{
  "mcpServers": {
    "gauntlet": {
      "command": "gauntlet",
      "args": ["mcp"]
    }
  }
}

{
  "mcpServers": {
    "gauntlet": {
      "command": "gauntlet",
      "args": ["mcp"]
    }
  }
}

Step 4 — verify. Restart the MCP client, then ask the AI to run gauntlet. The completion message will end with:

--- Save status ---
  [ok] Local history: saved
  [ok] Community dashboard: saved

If you see [skip] Community dashboard: skipped: SUPABASE_URL ... not set, your env file isn’t being loaded. Run gauntlet doctor in a terminal to see exactly what gauntlet is detecting, where it looked, and what’s reachable.

MCP Data Quality

When gauntlet runs as a desktop subprocess (the local setup above), it captures your real CPU, RAM, GPU, and OS just like gauntlet run does, and submissions go through the same validation pipeline.

When gauntlet runs as a Vercel-hosted endpoint (gauntlet.basaltlabs.app/mcp), the host is serverless infrastructure — no real hardware to fingerprint. Those rows are tagged os_platform=serverless and the model name is self-reported by the AI (not verified). For research-grade community data, prefer either local MCP or gauntlet run.

CI/CD Integration

Gate deployments on behavioral reliability. If a model update introduces behavioral regressions, the pipeline fails.

# Basic CI check (exits 0 on pass, 1 on fail)
gauntlet ci ollama/qwen3.5:4b --threshold 70 --trust-threshold 60

# JSON output for programmatic consumption
gauntlet ci ollama/qwen3.5:4b --format json --output results.json

# GitHub Actions annotations (warnings/errors in PR diffs)
gauntlet ci ollama/qwen3.5:4b --format github

# Fail on any critical safety probe failure
gauntlet ci ollama/qwen3.5:4b --fail-on-critical

# Quick mode for faster CI runs
gauntlet ci ollama/qwen3.5:4b --quick

GitHub Actions Example

- name: Behavioral regression check
  run: |
    pip install gauntlet-cli
    gauntlet ci ollama/qwen3.5:4b \
      --threshold 80 \
      --trust-threshold 70 \
      --fail-on-critical \
      --format github

Installation

pip install gauntlet-cli

Optional extras:

pip install gauntlet-cli[stats]          # scipy for precise confidence intervals
pip install gauntlet-cli[anthropic]      # Anthropic provider
pip install gauntlet-cli[openai]         # OpenAI provider
pip install gauntlet-cli[google]         # Google AI provider
pip install gauntlet-cli[all-providers]  # All cloud providers

Requirements:

• Python 3.10+
• At least one model provider:

Ollama, LM Studio, and llama.cpp run models locally with zero external dependency. Cloud providers are optional and can be combined with local models.

LM Studio

Gauntlet supports LM Studio via its OpenAI-compatible local server. Load a model in LM Studio, start the server under Developer > Local Server, then use the lmstudio: prefix:

# Default host: http://localhost:1234
gauntlet discover                                # lists currently-loaded models
gauntlet run --model lmstudio/llama-3.2-8b-q4_K_M

# Custom port (LM Studio lets users change it in-app)
export LMSTUDIO_HOST=http://localhost:4321
gauntlet run --model lmstudio/qwen-7b

# Or persist it
gauntlet config --lmstudio-host=http://localhost:4321

Cloud Baselines

Gauntlet can run the suite directly against OpenAI, Anthropic, and Google Gemini APIs so leaderboard entries for frontier models sit alongside local runs on comparable axes. Export an API key and use the provider prefix:

# Google Gemini (free tier available at https://aistudio.google.com/apikey)
export GOOGLE_API_KEY=AI...
gauntlet run --model google/gemini-2.5-flash
gauntlet run --model google/gemini-2.5-pro

# OpenAI
export OPENAI_API_KEY=sk-...
gauntlet run --model openai/gpt-4o-mini
gauntlet run --model openai/gpt-4o

# Anthropic Claude
export ANTHROPIC_API_KEY=sk-ant-...
gauntlet run --model anthropic/claude-haiku-4-5
gauntlet run --model anthropic/claude-sonnet-4-6

A full frontier sweep (6 models across 3 providers) typically costs under $5. Gemini Flash is free on the API’s free tier.

llama.cpp

Gauntlet supports llama.cpp via its OpenAI-compatible server API. Start llama-server with any GGUF model, then use the llamacpp: prefix:

# Start llama-server (default port 8080)
llama-server -m path/to/qwen3-8b-q4_K_M.gguf --port 8080

# Run benchmark
gauntlet run llamacpp:qwen3-8b-Q4_K_M

# Compare with an Ollama model
gauntlet compare llamacpp:qwen3-8b-Q4_K_M ollama:qwen3.5:4b "explain recursion"

# Custom host/port
export LLAMACPP_HOST=http://localhost:9090
gauntlet run llamacpp:my-model

The model name after llamacpp: is used for labeling in results and the leaderboard (llama-server serves whatever GGUF was loaded at startup). Use descriptive names like llamacpp:qwen3-8b-Q4_K_M so leaderboard entries are identifiable. Gauntlet auto-detects quantization, parameter size, and model family from the GGUF filename via /props.

CLI Reference

# Launch the interactive TUI
gauntlet

# Run the full benchmark (240 probes)
gauntlet run --model ollama/qwen3.5:4b --profile assistant

# Quick mode (~51 probes, reduced set per module)
gauntlet run --model ollama/qwen3.5:4b --quick

# Run a specific behavioral module
gauntlet run --model ollama/qwen3.5:4b --module sycophancy_gradient

# Compare two models head-to-head
gauntlet run --model ollama/qwen3.5:4b --model ollama/gemma4:e2b

# Domain-aware comparative evaluation
gauntlet compare gemma4:e2b qwen3.5:4b "build a CRM with Supabase auth and RLS"
gauntlet compare gemma4:e2b qwen3.5:4b "analyze this CSV for sales trends"
gauntlet compare gemma4:e2b qwen3.5:4b "write a Google Apps Script to sync calendar"

# Sequential mode (lower memory, suitable for 8GB machines)
gauntlet compare gemma4:e2b qwen3.5:4b "explain recursion" --seq

# Launch the web dashboard
gauntlet dashboard

# CI/CD gate (exit code 0 = pass, 1 = fail)
gauntlet ci ollama/qwen3.5:4b --threshold 80 --fail-on-critical

# Generate shields.io badge URL
gauntlet badge

# List installed models
gauntlet discover

# View persistent rankings
gauntlet leaderboard

Data and Privacy

Gauntlet transmits benchmark scores and anonymous hardware metadata to the community leaderboard. Here is exactly what is and is not sent:

All scoring executes locally. Probes, verification, and grading run on your machine. Only final numeric scores and the hardware class metadata above are transmitted.

Why hardware metadata? It enables community filtering. Without it, results from a 128GB cloud GPU and an 8GB laptop are averaged together, producing metrics representative of neither configuration. With hardware metadata, users can filter for “Apple Silicon, Q4, 16GB” and see results relevant to their setup.

MCP sessions use temporary server-side state, automatically deleted on completion or after 1 hour (pg_cron). MCP results are stored separately from community hardware results.

How data reaches the leaderboard: When you run gauntlet run or gauntlet compare, your CLI submits the score summary and hardware metadata to the Gauntlet server via a background HTTP request. This is non-blocking (never delays your CLI) and non-fatal (if the network is down, your test still completes normally). No credentials or accounts are needed on your machine.

Related Work

Gauntlet addresses limitations in existing evaluation frameworks:

Key differentiators: (1) TrustScore uses fully deterministic verification for all 214 behavioral probes (no LLM-as-judge). The Quick Test uses an external LLM judge for writing quality when an API key is available. The compare feature uses lightweight LLM evaluation for freeform prompts; (2) multi-turn behavioral protocols (sycophancy gradient, temporal coherence, instruction decay); (3) dynamic probe factories preventing benchmark contamination through memorization; (4) novel evaluation dimensions (confidence calibration via ECE, instruction decay rate, pressure threshold mapping); (5) community-aggregated results with hardware metadata, enabling filterable cross-hardware comparison that no single-lab benchmark can provide; (6) hardware tier classification with statistical rigor (confidence intervals, outlier detection) and collaborative filtering for performance prediction across untested configurations; (7) certification program (Gold/Silver/Bronze) providing standardized trust signals for model selection.

Contributing

We welcome contributions in the following areas:

• New probes: behavioral probes for existing categories
• New categories: proposals for unmeasured behavioral dimensions
• New factories: dynamic probe generators with per-run randomization
• Verification patterns: improved regex/keyword patterns for deterministic scoring
• Empirical results: large-scale evaluation results across model families

See CONTRIBUTING.md for details.

License

MIT

Built by Basalt Labs
_{The trust layer for AI. Community-driven behavioral research across real hardware.}