diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 092f7e9..b2a0e19 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -37,6 +37,8 @@ jobs: run: scripts/eceb-conformance.sh - name: Replay public ECEB study run: scripts/eceb-study-check.sh + - name: Replay adaptive ECEB study + run: scripts/eceb-adaptive-study-check.sh - name: Build documentation run: cargo doc --all-features --no-deps --locked - name: Package diff --git a/.github/workflows/pages.yml b/.github/workflows/pages.yml index 272806f..fd7b0c4 100644 --- a/.github/workflows/pages.yml +++ b/.github/workflows/pages.yml @@ -30,6 +30,7 @@ jobs: cp -a site/. _site/ cp spec/symgliph-context-glyph-v0.1.md _site/spec/SCGP-0.1.md cp spec/eceb-v0.1.md _site/spec/ECEB-0.1.md + cp spec/adaptive-context-v0.2.md _site/spec/SACP-0.2.md - name: Configure Pages uses: actions/configure-pages@v5 - name: Upload Pages artifact diff --git a/CHANGELOG.md b/CHANGELOG.md index 7556179..4582c79 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -16,6 +16,10 @@ the project intends to use Semantic Versioning after its first stable release. conformance seed, executable evaluator, and negative policy/provenance gates. - Deterministic 30-task Multi-SWE-bench Rust study suite, four-arm runner, comparative study schemas, raw golden run records, and offline replay gate. +- Experimental SACP-0.2 adaptive context profile, deterministic expansion + engine, CLI and JSON-RPC entry points, auditable stopping trace, schema, and + optional Multi-SWE-bench study arm. The replay evaluator binds every trace to + its declared ladder and sufficiency policy and rejects premature limit claims. ### Changed @@ -34,5 +38,12 @@ the project intends to use Semantic Versioning after its first stable release. - The Multi-SWE-bench Rust v0.1 study reduced paired prompt tokens by 97.58% and localization-model spend by 98.54% with Symgliph compiled context, while exact fix-path recall declined from 53.32% to 41.76%; lexical BM25 retained 46.87%. +- A retrieval-only adaptive threshold sweep selected 50% query-term coverage as + the lowest tested setting that retained the fixed compiled arm's 51.85% + evidence recall. This is in-sample calibration, not model-quality evidence. +- The adaptive 30-task follow-up retained 51.85% evidence recall, reduced mean + compiled prompt tokens from 5,138 to 4,345, improved exact fix-path recall + from 41.76% to 45.09%, and reduced recorded localization spend from $0.0668 + to $0.0573. It remains below BM25 and full-context recall. [Unreleased]: https://github.com/copyleftdev/symgliph/commits/main diff --git a/README.md b/README.md index a36f5e8..75e47c6 100644 --- a/README.md +++ b/README.md @@ -71,6 +71,39 @@ this run, but it did **not** preserve quality parity: exact fix-path recall was lexical BM25. The study supports the efficiency hypothesis and identifies retrieval quality as the next research problem. +## Adaptive context experiment + +The experimental SACP-0.2 profile addresses that gap by walking an ascending +token and semantic-node ladder. It stops at the first packet that passes a +declared evidence-sufficiency heuristic, when candidates are exhausted, or when +hard limits are reached. Every attempted context digest and stopping condition +is retained for evaluation. + +```text +query -> retrieve -> pack -> estimate sufficiency + ├── pass: emit packet + └── fail: expand budget and evidence +``` + +The built-in estimator measures query-term coverage, evidence count, and +optional excerpt completeness. It is deliberately named `estimated_sufficient`: +passing it does not establish retrieval recall or answer quality. A follow-up +Multi-SWE-bench run tested the policy against full context, BM25, and the fixed +compiled arm. + +The provisional 50% query-term threshold was selected by an exploratory replay +of the existing 30-task traces. It preserved the fixed compiled arm's evidence +recall while reducing selected context, but it is not independent validation. +See the [calibration record](docs/adaptive-calibration.md). + +In that follow-up, adaptive context retained the fixed arm's 51.85% evidence +recall while reducing mean prompt tokens from 5,138 to 4,345 and improving +exact fix-path recall from 41.76% to 45.09%. It still trailed BM25 and full +context. Read the [adaptive study and claim boundary](docs/eceb-adaptive-study.md). + +Read the experimental +[`Adaptive Context Compilation 0.2`](spec/adaptive-context-v0.2.md) profile. + Read the [methodology and claim boundary](docs/eceb-multiswe-study.md), inspect the [raw report](benchmarks/results/eceb-multiswe-rust-v0.1/report.json), or recompute it without API access: @@ -90,6 +123,13 @@ cargo run -- pack ./my-corpus \ --query "where is authorization checked?" \ --max-tokens 4000 +# Start small and expand until the declared evidence estimator passes. +cargo run -- adaptive-pack ./my-corpus \ + --query "where is authorization checked?" \ + --min-tokens 1000 \ + --max-tokens 8000 \ + --require-sufficient + # Recompute the corpus and semantic roots against current source. cargo run -- verify ./my-corpus ``` @@ -126,9 +166,13 @@ Every evidence record identifies its semantic node, source path, byte range, full-file digest, and excerpt digest. If a selected file changed after compilation, packing fails rather than emitting stale evidence. +`ContextEngine::pack_adaptive` accepts an `AdaptiveContextRequest` and returns +the selected packet plus the complete expansion trace. A failed estimate still +returns a valid verified packet unless the caller elects fail-closed behavior. + For local model and agent bridges, `symgliph serve` exposes -`context.describe`, `context.pack`, and `context.proof` over newline-delimited -JSON-RPC: +`context.describe`, `context.pack`, `context.adaptive`, and `context.proof` over +newline-delimited JSON-RPC: ```json {"jsonrpc":"2.0","id":1,"method":"context.pack","params":{"query":"authorization checks","max_tokens":2000,"max_nodes":8}} @@ -160,6 +204,7 @@ Run the independent gates: spec/validate.sh # SCGP artifact and packet conformance scripts/eceb-conformance.sh # ECEB policy, provenance, and budget gates scripts/eceb-study-check.sh # replay the public comparative study +scripts/eceb-adaptive-study-check.sh # replay the adaptive follow-up ``` ## Artifact model @@ -202,6 +247,12 @@ project code or build scripts. Additional analyzers can implement the A/B runs, Fabric instruction recovery, and blind semantic discovery. - [ECEB-0.1](spec/eceb-v0.1.md): a quality-, policy-, provenance-, and budget-aware measurement protocol. +- [SACP-0.2](spec/adaptive-context-v0.2.md): deterministic expansion, explicit + stopping semantics, and auditable evidence-sufficiency estimates. +- [Adaptive calibration](docs/adaptive-calibration.md): threshold sweep, + negative findings, and the preregistered next-run setting. +- [Adaptive ECEB study](docs/eceb-adaptive-study.md): the prospective model + run, complete traces, measured improvement, and remaining quality gap. - [Multi-SWE-bench Rust study](docs/eceb-multiswe-study.md): the current four-arm public result and raw observations. - [Semantic-routing golden set](docs/golden-set.md): linked corpora, queries, diff --git a/benchmarks/results/eceb-multiswe-rust-adaptive-v0.2/full_context.json b/benchmarks/results/eceb-multiswe-rust-adaptive-v0.2/full_context.json new file mode 100644 index 0000000..ccc6190 --- /dev/null +++ b/benchmarks/results/eceb-multiswe-rust-adaptive-v0.2/full_context.json @@ -0,0 +1,4779 @@ +{ + "schema": "symgliph.eceb/study-run-v0.1", + "suite_id": "symgliph-eceb-multiswe-rust/v0.1", + "strategy": "full_context", + "configuration": { + "implementation": "0.1.0", + "localization_metric": "exact repository-relative fix-path precision and recall", + "localizer_model": "openai/gpt-4.1-mini", + "localizer_output_tokens": "1024", + "localizer_profile": "json-paths/reasoning-none/v1", + "localizer_reasoning": "none", + "scope": "all non-binary corpus files", + "tokenizer": "o200k_base" + }, + "observations": [ + { + "task_id": "BurntSushi__ripgrep-1294", + "strategy": "full_context", + "repetition": 1, + "corpus_root": "fd8bc06d528150131e1be48415f354feccdcd187f420b18f402e0d4a69a5a193", + "context_blake3": "c8e3ec3d55003d81df82e0e733b6390ffc0ad97c3a54d2e2dc0d3ab1cdde0be1", + "prompt_tokens": 673257, + "selected_paths": [ + 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"retrieval_cost_microusd": 0, + "predicted_paths": [ + "tracing-attributes/src/lib.rs" + ], + "model_usage": { + "model": "openai/gpt-4.1-mini", + "input_tokens": 7407, + "output_tokens": 20, + "cost_microusd": 2995, + "latency_ms": 1157 + } + }, + { + "task_id": "tokio-rs__tracing-1983", + "strategy": "symgliph_compiled", + "repetition": 1, + "corpus_root": "06ba56c82148d88dbaaa587719eec1eb2c3e09fbd57eea4daa763cdcaab918a2", + "context_blake3": "870cdc8a68fb914cc19b57d34887ea3068ef1443dd0596e4854986c6f190254e", + "prompt_tokens": 2950, + "selected_paths": [ + "tracing-subscriber/src/filter/filter_fn.rs", + "CONTRIBUTING.md", + "tracing-subscriber/src/layer/mod.rs", + "tracing-subscriber/CHANGELOG.md" + ], + "retrieval_ms": 449, + "retrieval_input_tokens": 0, + "retrieval_cost_microusd": 0, + "predicted_paths": [ + "tracing-subscriber/src/filter/env_filter.rs" + ], + "model_usage": { + "model": "openai/gpt-4.1-mini", + "input_tokens": 3170, + "output_tokens": 22, + "cost_microusd": 1303, + "latency_ms": 1236 + } + }, + { + "task_id": "tokio-rs__tracing-2008", + "strategy": "symgliph_compiled", + "repetition": 1, + "corpus_root": "279eabbc35405f5cd49081e5765b0acdaf0dac0daf3384ed38245dc437bdce6e", + "context_blake3": "3d7c7b477866b4a06335ff4d6266cfda91690238f8bde25d9ebea12eafd9112d", + "prompt_tokens": 2654, + "selected_paths": [ + "tracing-subscriber/src/filter/env/mod.rs", + "tracing-log/src/lib.rs", + "tracing-subscriber/src/subscribe/mod.rs", + "tracing-subscriber/src/filter/targets.rs" + ], + "retrieval_ms": 825, + "retrieval_input_tokens": 0, + "retrieval_cost_microusd": 0, + "predicted_paths": [ + "tracing-subscriber/src/subscribe/mod.rs" + ], + "model_usage": { + "model": "openai/gpt-4.1-mini", + "input_tokens": 3365, + "output_tokens": 22, + "cost_microusd": 1381, + "latency_ms": 1111 + } + }, + { + "task_id": "tokio-rs__tracing-2090", + "strategy": "symgliph_compiled", + "repetition": 1, + "corpus_root": "14dca41ec9cd65c3127eff85b4d9fe47b26926b03a8c8032dfc3962458c951a3", + "context_blake3": "efb5e5bc5760c0ec93dca54392079ce6950cea080e680a8951a93998e0b03d31", + "prompt_tokens": 6315, + "selected_paths": [ + "tracing-attributes/src/expand.rs", + "CONTRIBUTING.md", + "README.md", + "tracing-attributes/tests/async_fn.rs" + ], + "retrieval_ms": 870, + "retrieval_input_tokens": 0, + "retrieval_cost_microusd": 0, + "predicted_paths": [ + "tracing-attributes/src/expand.rs" + ], + "model_usage": { + "model": "openai/gpt-4.1-mini", + "input_tokens": 6650, + "output_tokens": 21, + "cost_microusd": 2694, + "latency_ms": 950 + } + }, + { + "task_id": "tokio-rs__tracing-2335", + "strategy": "symgliph_compiled", + "repetition": 1, + "corpus_root": "cad865ecb42d1fa316a89519fad69df58a742ab220e639b9aa1c05e39f734184", + "context_blake3": "54bd78532f6105633f1b376de150b5c74e9aa1bf6b4e7e06f0b44c98da6382e1", + "prompt_tokens": 7836, + "selected_paths": [ + "tracing/src/lib.rs", + "tracing-attributes/src/lib.rs", + "tracing/README.md", + "tracing-attributes/src/attr.rs", + "tracing-core/src/metadata.rs", + "tracing-appender/src/rolling/builder.rs", + "tracing-opentelemetry/src/subscriber.rs" + ], + "retrieval_ms": 828, + "retrieval_input_tokens": 0, + "retrieval_cost_microusd": 0, + "predicted_paths": [ + "tracing-attributes/src/attr.rs", + "tracing-attributes/src/lib.rs" + ], + "model_usage": { + "model": "openai/gpt-4.1-mini", + "input_tokens": 8109, + "output_tokens": 31, + "cost_microusd": 3293, + "latency_ms": 1237 + } + }, + { + "task_id": "tokio-rs__tracing-2883", + "strategy": "symgliph_compiled", + "repetition": 1, + "corpus_root": "7fea9ea5e9e8bbabeba55ad92fcac53e29664f2c3c54b47fb5857aafe06e00bf", + "context_blake3": "22e07a4d6fbce50c216279e95c6233f2e65139e8679ba1588129a533e577ac8d", + "prompt_tokens": 5648, + "selected_paths": [ + "tracing/src/lib.rs", + "tracing/CHANGELOG.md", + "CONTRIBUTING.md", + "tracing-subscriber/src/fmt/format/json.rs", + "tracing-core/src/span.rs", + "tracing-core/src/parent.rs", + "tracing-core/src/event.rs" + ], + "retrieval_ms": 888, + "retrieval_input_tokens": 0, + "retrieval_cost_microusd": 0, + "predicted_paths": [ + "tracing/src/macros.rs", + "tracing/tests/macros.rs" + ], + "model_usage": { + "model": "openai/gpt-4.1-mini", + "input_tokens": 6254, + "output_tokens": 28, + "cost_microusd": 2546, + "latency_ms": 1109 + } + } + ] +} \ No newline at end of file diff --git a/docs/adaptive-calibration.md b/docs/adaptive-calibration.md new file mode 100644 index 0000000..67abf28 --- /dev/null +++ b/docs/adaptive-calibration.md @@ -0,0 +1,59 @@ +# Adaptive stopping calibration + +Status: **exploratory calibration; not a quality result** +Suite: Multi-SWE-bench Rust v0.1, 30 tasks +Date: 2026-08-03 + +## Question + +SACP-0.2 needs a preregistered evidence-sufficiency threshold before a paid +model run. We replayed deterministic adaptive traces at six query-term +coverage thresholds while holding the following values fixed: + +- minimum evidence excerpts: 2; +- token ladder: 1,000 through 8,000 in 1,000-token steps; +- node ladder: 2 through 16 in two-node steps; +- BM25 candidate paths followed by verified semantic expansion; and +- no model, embedding, or judge calls. + +For each threshold, the selected packet was the first passing round or the +final hard-limit round. Gold fix paths were used only after retrieval to score +the recorded rounds; they were never supplied to the compiler. + +## Result + +| Coverage threshold | Estimated stops | Mean prompt tokens | Mean token savings | Evidence recall | Complete-evidence tasks | +| ---: | ---: | ---: | ---: | ---: | ---: | +| 30% | 25/30 | 2,442 | 98.64% | 47.31% | 9/30 | +| 40% | 17/30 | 3,445 | 98.12% | 49.37% | 9/30 | +| **50%** | **12/30** | **4,344** | **97.81%** | **51.85%** | **9/30** | +| 60% | 5/30 | 4,957 | 97.65% | 51.85% | 9/30 | +| 70% | 0/30 | 5,138 | 97.59% | 51.85% | 9/30 | +| 75% | 0/30 | 5,138 | 97.59% | 51.85% | 9/30 | + +The fixed compiled arm also achieved 51.85% evidence recall. The 50% threshold +was therefore the lowest tested threshold that retained that retrieval metric +while selecting less context. It became the provisional default for the +prospective model run reported in +[`eceb-adaptive-study.md`](eceb-adaptive-study.md). + +## Important negative findings + +- The original 75% threshold never passed and produced 30/30 hard-limit stops. +- Nine tasks contained all gold paths at an earlier round, averaging 1,533 + prompt tokens, but the 75% estimator missed every one. +- At 50%, ten estimated-sufficient tasks still lacked complete gold evidence. + The estimator therefore cannot be interpreted as evidence completeness. +- Twenty-one tasks never contained every gold path at any recorded round. No + stopping policy can recover evidence that the underlying candidate generator + never retrieved. + +## Claim boundary + +This calibration reuses the same 30 tasks that exposed the original retrieval +gap. It is parameter selection, not independent validation. It does not measure +answer correctness, patch success, or model-quality noninferiority. + +The 50% threshold was frozen before the follow-up model observations were +collected. A general quality claim still requires a held-out task suite without +retuning this threshold, followed by replication across models and corpora. diff --git a/docs/context.md b/docs/context.md index 6d60983..b4584aa 100644 --- a/docs/context.md +++ b/docs/context.md @@ -27,6 +27,35 @@ For request `q`, budget `B`, manifest `M`, and current source tree `X`, Source blocks are labelled as untrusted data. Provenance protects integrity; it does not make repository text safe as model instructions. +## Adaptive compilation + +`ContextEngine::pack_adaptive` runs the ordinary verified packer across an +ascending token and semantic-node ladder. Each round records its ceilings, +actual prompt tokens, new evidence count, exact-context BLAKE3 digest, and a +deterministic evidence-sufficiency estimate. + +The reference estimator checks query-term coverage, minimum evidence count, +and—when requested—whether all included excerpts are complete. It stops with +one of three explicit outcomes: + +- `estimated_sufficient`: the declared heuristic passed; +- `candidate_exhausted`: all candidates fit but the heuristic failed; or +- `hard_limit_reached`: omitted material remained at both hard ceilings. + +This loop reduces needless fixed-budget padding and makes expansion behavior +auditable. It does not observe model correctness, so estimator success is not +quality verification. The experimental contract is +[`SACP-0.2`](../spec/adaptive-context-v0.2.md); ECEB remains the independent +quality and noninferiority layer. + +```console +cargo run -- adaptive-pack . \ + --query "where is retry policy enforced?" \ + --min-tokens 1000 \ + --max-tokens 8000 \ + --require-sufficient +``` + ## Measured proof On 2026-08-01, this repository was compiled and queried with: diff --git a/docs/eceb-adaptive-study.md b/docs/eceb-adaptive-study.md new file mode 100644 index 0000000..8a03bd4 --- /dev/null +++ b/docs/eceb-adaptive-study.md @@ -0,0 +1,94 @@ +# ECEB adaptive context study v0.2 + +Status: **experimental result; not a completeness claim** +Date: 2026-08-03 + +## Result + +This follow-up adds SACP-0.2 adaptive context to the same 30 pinned +Multi-SWE-bench Rust tasks and `openai/gpt-4.1-mini` file-localization profile +used by the v0.1 study. It retains the original control observations and adds +one new adaptive response per task. + +| Strategy | Mean prompt tokens | Token savings | Evidence recall | Fix-path recall | Recorded localization spend | +| --- | ---: | ---: | ---: | ---: | ---: | +| Full context | 380,856 | 0.00% | 100.00% | 53.32% | $4.5755 | +| Lexical BM25 | 7,677 | 95.82% | 58.55% | 46.87% | $0.0974 | +| Fixed compiled | 5,138 | 97.58% | 51.85% | 41.76% | $0.0668 | +| **Adaptive compiled** | **4,345** | **97.80%** | **51.85%** | **45.09%** | **$0.0573** | + +Relative to fixed compiled context, the adaptive arm selected 15.44% fewer +mean prompt tokens, improved exact fix-path recall by 3.33 percentage points, +and reduced recorded localization spend by 14.23%. Evidence recall was +unchanged. + +Adaptive context still trailed lexical BM25 fix-path recall by 1.78 percentage +points and full context by 8.23 points. The result therefore improves the +efficiency/quality tradeoff of the compiled arm but does not establish quality +parity. + +## Adaptive behavior + +The preregistered stopping policy used: + +- 50% query-term coverage; +- at least two evidence excerpts; +- token ceilings from 1,000 through 8,000 in 1,000-token steps; and +- node ceilings from 2 through 16 in two-node steps. + +Long queries whose packet kernel exceeded 1,000 tokens began at the minimum +kernel size while retaining the 8,000-token hard maximum. + +| Stop outcome | Tasks | +| --- | ---: | +| Estimated sufficient | 12 | +| Hard limit reached | 18 | +| Candidate exhausted | 0 | + +The mean selected round was 7.03. Four tasks stopped at round 4, three at round +5, two at round 6, and twenty-one selected round 8. Every observation records +the complete ladder, source paths, prompt counts, context digests, matched and +missing terms, and stop reason. + +## Method and claim boundary + +The 50% threshold was selected by the exploratory sweep documented in +[`adaptive-calibration.md`](adaptive-calibration.md). Because calibration and +evaluation use the same 30 tasks, this is not an independent estimate of +generalization. The model was sampled once per task and arm, and the benchmark +measures file localization rather than patch correctness. + +This result supports a narrower statement: + +> On this fixed run, adaptive stopping improved the fixed compiled arm's token, +> cost, and localization-recall measurements without changing its evidence +> recall. + +It does not show that the sufficiency estimator detects complete evidence, that +adaptive context is universally better than BM25, or that answer quality is +preserved on other corpora or models. + +## Reproduction + +The raw observations and report are checked into +[`benchmarks/results/eceb-multiswe-rust-adaptive-v0.2/`](../benchmarks/results/eceb-multiswe-rust-adaptive-v0.2/). +Replay all schemas, metrics, context-trace invariants, and negative +selected-round and premature-hard-limit tests without API access: + +```console +scripts/eceb-adaptive-study-check.sh +``` + +A fresh provider run requires OpenRouter credentials and spends API credit: + +```console +cargo run --features openrouter --bin symgliph-eceb-study -- run \ + benchmarks/eceb-multiswe-rust-v0.1.json \ + --strategies full,lexical,compiled,adaptive \ + --budget-tokens 8000 \ + --top-k 16 \ + --adaptive-min-query-term-coverage-bps 5000 \ + --localizer-model openai/gpt-4.1-mini \ + --localizer-output-tokens 1024 \ + --env-file /path/to/openrouter.env +``` diff --git a/docs/eceb-multiswe-study.md b/docs/eceb-multiswe-study.md index c5f277d..7dcf47c 100644 --- a/docs/eceb-multiswe-study.md +++ b/docs/eceb-multiswe-study.md @@ -10,6 +10,12 @@ quality parity. Symgliph used 97.58% fewer prompt tokens on the mean paired task and 98.54% less localization-model spend in aggregate, while exact fix-path localization recall declined by 11.56 percentage points versus full context. +A separate v0.2 follow-up adds the `symgliph_adaptive` arm defined by SACP-0.2. +It records the complete budget ladder and stop reason per task under the same +pinned suite, model, and localization profile. Its result remains separate so +this v0.1 record stays reproducible; see the +[adaptive study](eceb-adaptive-study.md). + ## Golden result | Strategy | Mean token savings | Evidence recall (95% CI) | Fix-path recall | Retrieval cost | Localization cost | diff --git a/scripts/eceb-adaptive-study-check.sh b/scripts/eceb-adaptive-study-check.sh new file mode 100755 index 0000000..c31d02b --- /dev/null +++ b/scripts/eceb-adaptive-study-check.sh @@ -0,0 +1,99 @@ +#!/usr/bin/env bash +set -euo pipefail + +repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +suite="$repo_root/benchmarks/eceb-multiswe-rust-v0.1.json" +results="$repo_root/benchmarks/results/eceb-multiswe-rust-adaptive-v0.2" +temporary="$(mktemp -d)" +trap 'rm -rf "$temporary"' EXIT + +if command -v check-jsonschema >/dev/null 2>&1; then + schema_validate() { + check-jsonschema --schemafile "$1" "${@:2}" + } +elif command -v jsonschema >/dev/null 2>&1; then + schema_validate() { + local schema="$1" + shift + local arguments=() + for instance in "$@"; do + arguments+=(--instance "$instance") + done + PYTHONWARNINGS=ignore::DeprecationWarning jsonschema "${arguments[@]}" "$schema" + } +else + echo "error: install check-jsonschema or jsonschema for study validation" >&2 + exit 1 +fi + +runs=( + "$results/full_context.json" + "$results/lexical_bm25.json" + "$results/symgliph_compiled.json" + "$results/symgliph_adaptive.json" +) + +schema_validate "$repo_root/spec/schema/eceb-study-suite-v0.1.schema.json" "$suite" +schema_validate "$repo_root/spec/schema/eceb-study-run-v0.1.schema.json" "${runs[@]}" +schema_validate "$repo_root/spec/schema/eceb-study-report-v0.1.schema.json" "$results/report.json" + +cargo run --quiet --manifest-path "$repo_root/Cargo.toml" --features openrouter \ + --bin symgliph-eceb-study -- evaluate "$suite" "${runs[@]}" \ + --output "$temporary/report.json" +cmp "$results/report.json" "$temporary/report.json" + +jq -e ' + .passed == true and + (.strategies | length) == 4 and + all(.strategies[]; .qualified_tasks == 30 and .total_tasks == 30 and (.failures | length) == 0) and + (.strategies[] | select(.strategy == "symgliph_adaptive") | + .token_savings.mean_bps == 9780 and + .evidence_recall.mean_bps == 5185 and + .mean_localization_recall_bps == 4509 and + .mean_localization_precision_bps == 7444 and + .total_localization_cost_microusd == 57316) +' "$results/report.json" >/dev/null + +jq -e ' + (.observations | length) == 30 and + all(.observations[]; + .adaptive_trace != null and + .adaptive_trace.selected_round == (.adaptive_trace.rounds | length) and + .prompt_tokens == .adaptive_trace.rounds[-1].prompt_tokens and + .context_blake3 == .adaptive_trace.rounds[-1].context_blake3 and + .selected_paths == .adaptive_trace.rounds[-1].selected_paths) and + ([.observations[] | select(.adaptive_trace.stop_reason == "estimated_sufficient")] | length) == 12 and + ([.observations[] | select(.adaptive_trace.stop_reason == "hard_limit_reached")] | length) == 18 +' "$results/symgliph_adaptive.json" >/dev/null + +jq '.observations[0].adaptive_trace.selected_round += 1' \ + "$results/symgliph_adaptive.json" >"$temporary/invalid-adaptive.json" +if cargo run --quiet --manifest-path "$repo_root/Cargo.toml" --features openrouter \ + --bin symgliph-eceb-study -- evaluate "$suite" \ + "$results/full_context.json" "$temporary/invalid-adaptive.json" \ + --output "$temporary/invalid-report.json" 2>"$temporary/invalid.stderr"; then + echo "error: adaptive study evaluator accepted a mismatched selected round" >&2 + exit 1 +fi +rg -q 'selected round .* does not equal final round' "$temporary/invalid.stderr" + +jq ' + (.observations | map(.adaptive_trace.stop_reason == "hard_limit_reached") | index(true)) as $index | + (.observations[$index].adaptive_trace.rounds[0]) as $round | + .observations[$index].adaptive_trace.rounds = [$round] | + .observations[$index].adaptive_trace.selected_round = 1 | + .observations[$index].prompt_tokens = $round.prompt_tokens | + .observations[$index].context_blake3 = $round.context_blake3 | + .observations[$index].selected_paths = $round.selected_paths +' "$results/symgliph_adaptive.json" >"$temporary/early-hard-limit.json" +if cargo run --quiet --manifest-path "$repo_root/Cargo.toml" --features openrouter \ + --bin symgliph-eceb-study -- evaluate "$suite" \ + "$results/full_context.json" "$temporary/early-hard-limit.json" \ + --output "$temporary/early-hard-limit-report.json" \ + 2>"$temporary/early-hard-limit.stderr"; then + echo "error: adaptive study evaluator accepted an early hard-limit claim" >&2 + exit 1 +fi +rg -q 'before reaching both configured maxima' "$temporary/early-hard-limit.stderr" + +echo "ECEB adaptive Multi-SWE-bench study: PASS" diff --git a/scripts/site-check.sh b/scripts/site-check.sh index 0020877..b603bc4 100755 --- a/scripts/site-check.sh +++ b/scripts/site-check.sh @@ -23,7 +23,8 @@ for target in \ "https://github.com/copyleftdev/symgliph" \ "spec/symgliph-context-glyph-v0.1.md" \ "spec/ECEB-0.1.md" \ - "https://github.com/copyleftdev/symgliph/blob/main/docs/eceb-multiswe-study.md"; do + "spec/SACP-0.2.md" \ + "https://github.com/copyleftdev/symgliph/blob/main/docs/eceb-adaptive-study.md"; do rg --fixed-strings "$target" "$site_root/index.html" >/dev/null || { echo "error: missing public target $target" >&2 exit 1 diff --git a/site/index.html b/site/index.html index bc35739..6b9dafb 100644 --- a/site/index.html +++ b/site/index.html @@ -294,20 +294,22 @@

Select, verify, and pack

Efficiency improved.
Recall did not hold.

Thirty pinned Multi-SWE-bench Rust tasks across ripgrep, fd, bytes, - and tracing. Four paired context strategies, one localization model, - one response per task and arm. Cold embedding overhead is included. + and tracing. The original four-arm study plus a separately recorded + adaptive follow-up used one localization model and one response per + task and arm. Cold embedding overhead is included where applicable.

- + - + + @@ -316,28 +318,32 @@

Efficiency improved.
Recall did not hold.

- + + - + + - + + - + +
ECEB Multi-SWE-bench Rust v0.1 resultsECEB Multi-SWE-bench Rust v0.1 results and adaptive v0.2 follow-up
Measure All files BM25 EmbeddingSymgliphFixedAdaptive
0.00% 95.82% 95.78%97.58%97.58%97.80%
Gold-path coverage in context 100.00% 58.55% 56.49%51.85%51.85%51.85%
Exact predicted fix-path recall 53.32% 46.87% 43.34%41.76%41.76%45.09%
Recorded API spend $4.5755 $0.0974 $0.3237$0.0668$0.0668$0.0573
@@ -345,20 +351,23 @@

Efficiency improved.
Recall did not hold.