LLM Benchmarking

[bradleyshep]

Description of Changes

Introduce a new LLM benchmarking app and supporting code.

• CLI: llm with subcommands run, routes list, diff, ci-check.
• Runner: executes globally numbered tasks; filters by --lang, --categories, --tasks, --providers, --models.
• Providers/clients: route layer (provider:model) with HTTP LLM Vendor clients; env-driven keys/base URLs.
• Evaluation: deterministic scorers (hash/equality, JSON shape/count, light schema/reducer parity) with clear failure messages.
• Results: stable JSON schema; single-file HTML viewer to inspect/filter/export CSV.
• Build & guards: build script for compile-time setup;
• Docs: DEVELOP.md includes cargo llm … usage.

This PR is the initial addition of the app and its modules (runner, config, routes, prompt/segmentation, scorers, schema/types, defaults/constants/paths/hashing/combine, publishers, spacetime guard, HTML stats viewer).

How it works

1. Pick what to run

   • Choose tasks (--tasks 0,7,12), or a language (--lang rust|csharp), or categories (--categories basics,schema).
   • Optionally limit vendors/models (--providers …, --models …).

2. Resolve routes

   • Read env (API keys + base URLs) and build the active set (e.g., openai:gpt-5).

3. Build context

   • Start Spacetime
   • Publish golden answer modules
   • Prepare prompts and send to LLM model
   • Attempt to publish LLM module

4. Execute calls

   • Run the selected tasks within each test against selected models and languages.

5. Score outputs

   • Apply deterministic scorers (hash/equality, JSON shape/count, simple schema/reducer checks).
   • Record the score and any short failure reason.

6. Update results file

   • Write/update the single results JSON with task/route outcomes, timings, and summaries.

API and ABI breaking changes

None. New application and modules; no existing public APIs/ABIs altered.

Expected complexity level and risk

4/5. New CLI, routing, evaluation, and artifact format.

• External model APIs may rate-limit/timeout; concurrency tunable via LLM_BENCH_CONCURRENCY / LLM_BENCH_ROUTE_CONCURRENCY.

Testing

I ran the full test matrix and generated results for every task against every vendor, model, and language (rust + C#). I also tested the CI check locally using act.

Please verify

• llm run --tasks 0,1,2 (explicit run)
• llm run --lang rust --categories basics (filters)
• llm run --categories basics,schema (multiple categories)
• llm run --lang csharp (language switch)
• llm run --providers openai,anthropic --models "openai:gpt-5 anthropic:claude-sonnet-4-5" (provider/model limits)
• llm run --hash-only (dry integrity)
• llm run --goldens-only (test goldens only)
• llm run --force (skip hash check)
• llm ci-check
• Stats viewer loads the JSON; filtering and CSV export work
• CI works as intended