{ "run_id": "phrugsa-limbunlom--deltaflow-claude-haiku-5-5-1793170996249152880", "date": "2026-10-28T07:11:42.108907+00:00", "repo": "phrugsa-limbunlom/deltaflow", "kind": "repo", "source": "https://github.com/phrugsa-limbunlom/deltaflow", "commit": "fdf4ba96cd87d6e57588126bcc9857db05303cd7", "model": "claude-haiku-5-5", "model_version": "claude-haiku-5-5", "test_version": "readme-a0d0e27a4d0fd4c50903587087c25799260ee2578ee1953a6356df3e4fd02c35", "pass": true, "outcome": "pass", "time_to_working_s": 463.08516926599987, "total_s": 505.8595585823059, "steps": [ "cd && pwd && ls -la && git status 2>&1 | head -3; python3 --version; cat README.md 2>/dev/null | head -c 7000", "ls /inputs 2>&1; echo \"CDP=$CDP_URL\"; curl -s -m 10 -w '\\nHTTP %{http_code}\\n' \"$CDP_URL/json/version\"", "cd && grep -n '^#' README.md; grep -n -i 'pip install\\|python_requires\\|requires-python\\|Python 3' README.md | head -30; grep -n requires-python pyproject.toml", "cd && sed -n '113,150p;326,345p' README.md", "cd && sed -n '/dependencies/,/^\\]/p' pyproject.toml | head -40; cat requirements.txt; python3 -m venv --help >/dev/null && echo VENV_OK || (sudo -n apt-get -o DPkg::Lock::Timeout=300 install -y python3.12-venv 2>&1 | tail -3)", "cd /tmp && python3 -m venv /tmp/dfvenv && /tmp/dfvenv/bin/pip install -q --upgrade pip 2>&1 | tail -2; cd && timeout 7000 /tmp/dfvenv/bin/pip install --index-url https://download.pytorch.org/whl/cpu torch 2>&1 | tail -3", "cd && timeout 7000 /tmp/dfvenv/bin/pip install -e \".[dev]\" 2>&1 | tail -4", "cd && find examples -maxdepth 2 | head -60; ls tests | head -30", "cd && ls examples/00-foundations/01-linear-interpolant examples/10-sampling/01-euler-flow examples/20-training/01-flow-matching; cat examples/20-training/01-flow-matching/README.md 2>/dev/null | head -60", "cd && cat examples/00-foundations/01-linear-interpolant/main.py | head -80", "cd && /tmp/dfvenv/bin/python examples/00-foundations/01-linear-interpolant/main.py; echo EXIT=$?", "ls -la 2>&1; ls ; grep -n 'python_api\\|python_example\\|probe_kind' | head -40", "sed -n '1057,1110p' ; sed -n '1185,1200p' ", "sed -n '915,960p' ; grep -n 'PYTHON_EXAMPLE_RUNNER\\s*=' -A3 | head -8", "cd && /tmp/dfvenv/bin/python -c \"import deltaflow.interpolants as i, deltaflow.losses as l, deltaflow.samplers as s, deltaflow.solvers as so; print(i.__all__ if hasattr(i,'__all__') else dir(i)); print(getattr(l,'__all__',dir(l))); print(getattr(s,'__all__',dir(s))); print(getattr(so,'__all__',dir(so)))\" 2>&1 | head -30; ls deltaflow", "cd && ls deltaflow/utils; grep -rn '^def ' deltaflow/utils deltaflow/interpolants deltaflow/losses | head -30; grep -n 'def decode' -A6 | head -12", "cd && grep -c '>>>' README.md; grep -n 'api_expectations\\s*+=\\|api_expectations =\\|expectation' | head -12; grep -rn '>>>' examples/*/*/main.py | head -3", "cd && time (timeout 900 /tmp/dfvenv/bin/python examples/20-training/01-flow-matching/main.py > /tmp/fm.out 2>&1; echo EXIT=$?); tail -15 /tmp/fm.out", "cd && timeout 900 /tmp/dfvenv/bin/python examples/10-sampling/01-euler-flow/main.py; echo EXIT=$?; timeout 900 /tmp/dfvenv/bin/python examples/30-inverse/01-posterior/main.py 2>&1 | tail -5; echo EXIT=$?", "cd && grep -ln 'manual_seed' examples/10-sampling/01-euler-flow/main.py examples/20-training/01-flow-matching/main.py examples/30-inverse/01-posterior/main.py; timeout 1800 /tmp/dfvenv/bin/pytest -q 2>&1 | tail -6; echo EXIT=${PIPESTATUS[0]}", "cd && grep -rniE 'cron|webhook|--once|--now|scheduler|while True' README.md deltaflow examples --include=*.py --include=*.md | head -10; grep -n 'scripts\\|entry-points\\|\\[project.scripts\\]' -A2 pyproject.toml | head; ls -la .coldpass", "cd && grep -c 'Every path returns the interpolated state \\$x_t\\$ and its conditional target velocity \\$u_t\\$\\.' README.md; grep -c 'tree is a tiered, runnable curriculum\\.' README.md; grep -c 'Regress a velocity field onto the conditional velocity of a probability path\\.' README.md; grep -c 'transporting the whole source density onto the data density with a simple Euler or Heun solver\\.' README.md; grep -c 'posterior sampling for an inverse problem, reusing the very same pretrained field\\.' README.md", "cd && cat > .coldpass/probe.json <<'EOF'\n{\"kind\":\"cli\",\"argv\":[\"/tmp/dfvenv/bin/python\",\"examples/10-sampling/01-euler-flow/main.py\"],\"cwd\":\"\",\"expected_text\":\"sample mean: [0.019584309309720993, 0.05306430160999298]\"}\nEOF\ncat > .coldpass/claims.json <<'EOF'\n[\n{\"kind\":\"claim\",\"claim\":\"Every probability path returns the interpolated state x_t and its conditional target velocity u_t (interpolant example prints x_t and u_t and passes boundary checks)\",\"source\":\"Every path returns the interpolated state $x_t$ and its conditional target velocity $u_t$.\",\"input\":\"examples/00-foundations/01-linear-interpolant/main.py\",\"expected\":\"x_t and u_t printed for each t, boundary conditions OK\",\"probe_kind\":\"cli\",\"argv\":[\"/tmp/dfvenv/bin/python\",\"examples/00-foundations/01-linear-interpolant/main.py\"],\"cwd\":\"\",\"expected_text\":\"Boundary conditions OK.\"},\n{\"kind\":\"claim\",\"claim\":\"The examples tree is a runnable curriculum (linear interpolant example runs)\",\"source\":\"The [`examples/`](https://github.com/phrugsa-limbunlom/deltaflow/tree/main/examples) tree is a tiered, runnable curriculum.\",\"input\":\"examples/00-foundations/01-linear-interpolant/main.py\",\"expected\":\"t=1.00 row printed with x_t endpoint equal to x1\",\"probe_kind\":\"cli\",\"argv\":[\"/tmp/dfvenv/bin/python\",\"examples/00-foundations/01-linear-interpolant/main.py\"],\"cwd\":\"\",\"expected_text\":\"t=1.00 x_t[0]=[1.5409960746765137, -0.293428897857666]\"},\n{\"kind\":\"claim\",\"claim\":\"Flow matching regresses a velocity field onto the conditional velocity of the path (training example runs and reports loss)\",\"source\":\"Regress a velocity field onto the conditional velocity of a probability path.\",\"input\":\"examples/20-training/01-flow-matching/main.py\",\"expected\":\"final loss printed\",\"probe_kind\":\"cli\",\"argv\":[\"/tmp/dfvenv/bin/python\",\"examples/20-training/01-flow-matching/main.py\"],\"cwd\":\"\",\"expected_text\":\"final loss: 4.1941\"},\n{\"kind\":\"claim\",\"claim\":\"A learned field is integrated with a simple Euler or Heun solver to produce samples\",\"source\":\"transporting the whole source density onto the data density with a simple Euler or Heun solver.\",\"input\":\"examples/10-sampling/01-euler-flow/main.py\",\"expected\":\"sample mean and std printed\",\"probe_kind\":\"cli\",\"argv\":[\"/tmp/dfvenv/bin/python\",\"examples/10-sampling/01-euler-flow/main.py\"],\"cwd\":\"\",\"expected_text\":\"sample mean: [0.019584309309720993, 0.05306430160999298]\"},\n{\"kind\":\"claim\",\"claim\":\"Posterior sampling for an inverse problem reuses the pretrained field and reports observed-region MSE\",\"source\":\"posterior sampling for an inverse problem, reusing the very same pretrained field.\",\"input\":\"examples/30-inverse/01-posterior/main.py\",\"expected\":\"observed-region MSE printed for independent-coupled model\",\"probe_kind\":\"cli\",\"argv\":[\"/tmp/dfvenv/bin/python\",\"examples/30-inverse/01-posterior/main.py\"],\"cwd\":\"\",\"expected_text\":\"observed-region MSE, independent-coupled model: 0.0633\"}\n]\nEOF\n/tmp/dfvenv/bin/python -c \"import json;json.load(open('.coldpass/claims.json'));json.load(open('.coldpass/probe.json'));print('json ok')\"", "cd && cat > .coldpass/examples.json <<'EOF'\n[\n{\"kind\":\"example\",\"claim\":\"Posterior sampling example runs with a pretrained field\",\"source\":\"examples/30-inverse/01-posterior/main.py\",\"input\":\"none\",\"expected\":\"observed-region MSE printed\",\"argv\":[\"/tmp/dfvenv/bin/python\",\"examples/30-inverse/01-posterior/main.py\"],\"cwd\":\"\",\"env\":{},\"expected_text\":\"observed-region MSE, independent-coupled model: 0.0633\"},\n{\"kind\":\"example\",\"claim\":\"Development test suite from README Development section passes\",\"source\":\"README.md Development\",\"input\":\"pytest\",\"expected\":\"tests pass\",\"argv\":[\"/tmp/dfvenv/bin/pytest\",\"-q\"],\"cwd\":\"\",\"env\":{},\"expected_text\":\"81 passed, 3 skipped\"}\n]\nEOF\ncat > .coldpass/cycles.json <<'EOF'\n{\"detected\":false,\"checks\":[],\"reason\":\"Inspected README.md and package entrypoints. DeltaFlow is a PyTorch library with no CLI, daemon, cron, webhook, or --once/--now scheduler. The only while True found is deltaflow/trainer/loop.py training loop, which is not a product cycle.\"}\nEOF\n/tmp/dfvenv/bin/python -c \"import json;[json.load(open('.coldpass/'+f)) for f in ('examples.json','cycles.json')];print('ok')\"; = timeout 3000 /tmp/dfvenv/bin/python -I --validate --replay 2>&1 | tail -60", "cd && cat > .coldpass/report.json <<'EOF'\n{\"outcome\":\"working\",\"fault_owner\":null,\"operation\":\"Install DeltaFlow from source per README 'Install' and run the documented Usage/Examples: linear interpolant, Euler sampling, flow-matching training, and posterior sampling examples, plus the README Development pytest suite.\",\"operation_scope\":\"Python library (torchdeltaflow / deltaflow) on CPU, Python 3.12.3 venv at /tmp/dfvenv, torch 2.14.1+cpu. No server, CLI, browser or credentials required. Shared CDP endpoint was reachable (HTTP 200) but not needed.\",\"steps\":[\"python3 -m venv /tmp/dfvenv\",\"/tmp/dfvenv/bin/pip install torch (CPU wheel from download.pytorch.org)\",\"cd && /tmp/dfvenv/bin/pip install -e \\\".[dev]\\\" (README Install)\",\"/tmp/dfvenv/bin/python examples/00-foundations/01-linear-interpolant/main.py\",\"/tmp/dfvenv/bin/python examples/10-sampling/01-euler-flow/main.py\",\"/tmp/dfvenv/bin/python examples/20-training/01-flow-matching/main.py\",\"/tmp/dfvenv/bin/python examples/30-inverse/01-posterior/main.py\",\"/tmp/dfvenv/bin/pytest -q (README Development)\"],\"deviations\":[\"Environment is WSL2-style Linux guest; python3 venv already available so no apt install was needed.\",\"Installed torch from the PyTorch CPU index before the editable install so no CUDA wheels were pulled; the README only says pip install.\",\"README usage snippets reference undefined model/x1 and are not runnable as-is; I ran the repository's runnable example scripts instead.\",\"PyPI 'pip install torchdeltaflow' not run; the from-source 'pip install -e \\\".[dev]\\\"' from the README was used.\",\"/inputs/coldpass-preflight.py does not exist in this guest; I ran the checker at with the same --validate --replay arguments.\",\"No scheduler/cycle exists; cycles.json has detected=false.\",\"python_api/python_example claims were not used: README code has no >>> outputs and public functions take tensors that cannot be passed as JSON, so claims are cli probes of the repo's example scripts.\"],\"stuck_step\":null,\"main_blocker\":null,\"plain_failure\":null,\"suggested_fix\":\"None required for this operation. Optional: make README usage snippets self-contained (define model and x1) so they can be run as-is.\"}\nEOF\n/tmp/dfvenv/bin/python -c \"import json;print(json.load(open('.coldpass/report.json'))['outcome'])\"; ls -la .coldpass" ], "stuck_step": null, "error_excerpt": null, "what_it_did": "Install DeltaFlow from source per README 'Install' and run the documented Usage/Examples: linear interpolant, Euler sampling, flow-matching training, and posterior sampling examples, plus the README Development pytest suite.", "suggested_fix": null, "tokens_in": 572844, "tokens_out": 14474, "cost_usd": 0.020334340000000003, "usage_complete": true, "checks": [ { "check": "operation", "status": "PASS", "details": { "working": true, "argv": [ "/tmp/dfvenv/bin/python", "examples/10-sampling/01-euler-flow/main.py" ], "kind": "cli", "returncode": 0, "stdout": "step= 0 loss=2.9880\nstep= 100 loss=1.7197\nstep= 200 loss=1.6778\nstep= 300 loss=1.6499\nstep= 400 loss=1.6405\nsample mean: [0.019584309309720993, 0.05306430160999298]\nsample std: [1.97209894657135, 0.18536506593227386]", "stderr": "", "needs_credentials": false, "needs_browser": false, "scope": "product operation" } } ], "safety": { "version": "z2x-1", "rules_version": "z2x-1", "summary": "Review signals: 38 known vulnerability.", "expected": "Reviewed as expected for a test run: attempted writes that failed, so nothing was written.", "findings": [ { "kind": "known-vulnerability", "count": 38, "note": "OSV reports a known vulnerability in this dependency version.", "location_count": 3 } ], "context": [], "limitations": [ "A short run cannot establish that code is safe.", "Connections show IP addresses; proxies can hide the final hostname.", "Tracing covers dispatched commands and their children, not the whole OS; guest root can interfere with tracing." ], "static": { "dependency_note": "OSV scanned supported lockfiles; unpinned dependencies and unsupported formats are outside this check." }, "evidence_url": "/safety/?run=phrugsa-limbunlom--deltaflow-claude-haiku-5-5-1793170996249152880", "evidence_file": "/data/safety/phrugsa-limbunlom--deltaflow-claude-haiku-5-5-1793170996249152880.json.gz" }, "agent_execution": "native_agent", "agent_runtime": "claude-code", "agent_runtime_version": "2.1.310 (Claude Code)", "tool_transport": "mcp-assigned-vm", "platform": "linux", "reasoning": "low", "service_tier": null, "resources": { "cpus": 2, "memory_mib": 4096, "disk_mib": 32768 }, "native_tool_calls": 25, "native_bundle": null, "architecture": null, "backend": "hyperv", "platform_details": { "backend": "hyperv", "os": "linux" }, "platform_conflicts": null, "sandbox_source": null, "source_pin": { "commit": "fdf4ba96cd87d6e57588126bcc9857db05303cd7", "url": "https://github.com/phrugsa-limbunlom/deltaflow" }, "purpose": "initial", "profile": { "model": "claude-haiku-5-5", "reasoning": "low" }, "fault": { "owner": null, "status": "resolved", "reason": null }, "fix_id": null, "verifies_run_id": null, "hosting": null, "operation_scope": "product operation", "tool_calls": 25, "guest_setup": null, "runtime": null, "runtime_version": null, "runtime_revision": null, "source_revision": null, "evidence_version": null, "failure_origin": null, "evidence_strength": "readme_checked", "works": "yes", "install_s": null, "first_use_s": null, "mcp": "none", "needs": [], "needs_which": null, "agent_interface": [ "cli" ], "license": "MIT", "repo_url": "https://github.com/phrugsa-limbunlom/deltaflow", "cost_to_try": "free", "hardware": null, "what_failed": null, "local_or_hosted": "local", "cycle_checks": [ { "kind": "cycle", "claim": "Scheduled or looping main behavior", "source": "repository entry points", "input": "source inspection", "expected": "identify the product trigger", "verdict": "yes", "outcome": "Inspected README.md and package entrypoints. DeltaFlow is a PyTorch library with no CLI, daemon, cron, webhook, or --once/--now scheduler. The only while True found is deltaflow/trainer/loop.py training loop, which is not a product cycle.", "evidence": "Inspected README.md and package entrypoints. DeltaFlow is a PyTorch library with no CLI, daemon, cron, webhook, or --once/--now scheduler. The only while True found is deltaflow/trainer/loop.py training loop, which is not a product cycle.", "command": "" } ], "claims": [ { "kind": "claim", "claim": "Every probability path returns the interpolated state x_t and its conditional target velocity u_t (interpolant example prints x_t and u_t and passes boundary checks)", "source": "Every path returns the interpolated state $x_t$ and its conditional target velocity $u_t$.", "input": "examples/00-foundations/01-linear-interpolant/main.py", "expected": "x_t and u_t printed for each t, boundary conditions OK", "command": "/tmp/dfvenv/bin/python examples/00-foundations/01-linear-interpolant/main.py", "verdict": "yes", "outcome": "Real input produced the expected result: Boundary conditions OK.", "evidence": "Boundary conditions OK.\n", "returncode": 0, "source_checked": true, "source_file": "README.md" }, { "kind": "claim", "claim": "The examples tree is a runnable curriculum (linear interpolant example runs)", "source": "The [`examples/`](https://github.com/phrugsa-limbunlom/deltaflow/tree/main/examples) tree is a tiered, runnable curriculum.", "input": "examples/00-foundations/01-linear-interpolant/main.py", "expected": "t=1.00 row printed with x_t endpoint equal to x1", "command": "/tmp/dfvenv/bin/python examples/00-foundations/01-linear-interpolant/main.py", "verdict": "yes", "outcome": "Real input produced the expected result: t=1.00 x_t[0]=[1.5409960746765137, -0.293428897857666]", "evidence": "t=1.00 x_t[0]=[1.5409960746765137, -0.293428897857666] u_t[0]=[2.260253667831421, 0.109914630651474]\nBoundary conditions OK.\n", "returncode": 0, "source_checked": true, "source_file": "README.md" }, { "kind": "claim", "claim": "Flow matching regresses a velocity field onto the conditional velocity of the path (training example runs and reports loss)", "source": "Regress a velocity field onto the conditional velocity of a probability path.", "input": "examples/20-training/01-flow-matching/main.py", "expected": "final loss printed", "command": "/tmp/dfvenv/bin/python examples/20-training/01-flow-matching/main.py", "verdict": "yes", "outcome": "Real input produced the expected result: final loss: 4.1941", "evidence": "final loss: 4.1941\n", "returncode": 0, "source_checked": true, "source_file": "README.md" }, { "kind": "claim", "claim": "A learned field is integrated with a simple Euler or Heun solver to produce samples", "source": "transporting the whole source density onto the data density with a simple Euler or Heun solver.", "input": "examples/10-sampling/01-euler-flow/main.py", "expected": "sample mean and std printed", "command": "/tmp/dfvenv/bin/python examples/10-sampling/01-euler-flow/main.py", "verdict": "yes", "outcome": "Real input produced the expected result: sample mean: [0.019584309309720993, 0.05306430160999298]", "evidence": "sample mean: [0.019584309309720993, 0.05306430160999298]\nsample std: [1.97209894657135, 0.18536506593227386]\n", "returncode": 0, "source_checked": true, "source_file": "README.md" }, { "kind": "claim", "claim": "Posterior sampling for an inverse problem reuses the pretrained field and reports observed-region MSE", "source": "posterior sampling for an inverse problem, reusing the very same pretrained field.", "input": "examples/30-inverse/01-posterior/main.py", "expected": "observed-region MSE printed for independent-coupled model", "command": "/tmp/dfvenv/bin/python examples/30-inverse/01-posterior/main.py", "verdict": "yes", "outcome": "Real input produced the expected result: observed-region MSE, independent-coupled model: 0.0633", "evidence": "\u2026observed-region MSE, independent-coupled model: 0.0633\n observed-region MSE, OT-coupled model: 0.0710\n\nNote: this toy setup is not tuned; it demonstrates the API wiring, not SOTA quality.\n", "returncode": 0, "source_checked": true, "source_file": "README.md" } ], "claims_note": null, "example_checks": [ { "kind": "example", "claim": "Posterior sampling example runs with a pretrained field", "source": "examples/30-inverse/01-posterior/main.py", "input": "none", "command": "/tmp/dfvenv/bin/python examples/30-inverse/01-posterior/main.py", "evidence": "/ 100 steps\n[deltaflow.trainer] step 300 | loss 0.1096 | 2.1s / 100 steps\n[deltaflow.trainer] step 100 | loss 0.1989 | 2.8s / 100 steps\n[deltaflow.trainer] step 200 | loss 0.0747 | 2.1s / 100 steps\n[deltaflow.trainer] step 300 | loss 0.0625 | 2.0s / 100 steps\n[2/3] Unconditional Euler sampling (sanity check)...\n[3/3] Posterior sampling under a centre-mask inpainting measurement...\n\n observed-region MSE, independent-coupled model: 0.0633\n observed-region MSE, OT-coupled model: 0.0710\n\nNote: this toy setup is not tuned; it demonstrates the API wiring, not SOTA quality.", "deferred": false, "expected": "observed-region MSE printed", "returncode": 0, "verdict": "yes", "outcome": "command completed successfully" }, { "kind": "example", "claim": "Development test suite from README Development section passes", "source": "README.md Development", "input": "pytest", "command": "/tmp/dfvenv/bin/pytest -q", "evidence": ".............................. [ 85%]\n............ [100%]\n=============================== warnings summary ===============================\ntests/test_trainer.py::test_train_loop_runs_and_checkpoints\n /tmp/dfvenv/lib/python3.12/site-packages/torch/utils/data/dataloader.py:759: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n super().__init__(loader)\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n81 passed, 3 skipped, 1 warning in 6.46s", "deferred": false, "expected": "tests pass", "returncode": 0, "verdict": "yes", "outcome": "command completed successfully" } ], "does_what_it_says": "yes", "maintenance": { "last_commit_at": "2026-10-28T04:24:39Z", "commits_90d": 71, "merged_prs_90d": 14, "open_issues": 3, "open_prs": 0, "reply_share": null, "reply_threads": 0, "median_reply_hours": null, "last_release_at": "2026-10-28T04:27:06Z", "archived": false, "verdict": "maintained", "note": "actively maintained: last commit 0 days ago; 71 commits and 14 merged PRs in 90 days; 3 open issues, 0 open PRs; latest release 2026-10-28" }, "maintenance_verdict": "maintained", "maintenance_note": "actively maintained: last commit 0 days ago; 71 commits and 14 merged PRs in 90 days; 3 open issues, 0 open PRs; latest release 2026-10-28", "maintained": "active", "maintained_note": "last commit 0 days ago; 3 open issues, 10 answered or closed", "last_commit_at": "2026-10-28T04:33:20Z", "last_release_at": "2026-10-28T04:27:06Z", "open_issues": 3, "answered_issues": 10, "platforms_claimed": [ "linux" ], "topic": "other", "agent_interfaces": [ { "interface": "cli", "evidence": "independent checker", "observed": true, "operation_checked": true } ], "agent_interfaces_note": "README metadata is a discovery hint, not pinned documentation proof. Observed interfaces were exercised in this run. An empty list means unknown.", "mcp_context": null, "tested_at": "2026-10-28T07:11:42.108907+00:00", "tested_commit": "fdf4ba96cd87d6e57588126bcc9857db05303cd7", "tested_with": { "agent": "claude-code", "agent_version": "2.1.310 (Claude Code)", "model": "claude-haiku-5-5", "model_route": "claude-haiku-5-5", "reasoning": "low", "os": "linux", "backend": "hyperv", "transport": "mcp-assigned-vm" }, "setup_identity": { "repo": "phrugsa-limbunlom/deltaflow", "source": "https://github.com/phrugsa-limbunlom/deltaflow", "commit": "fdf4ba96cd87d6e57588126bcc9857db05303cd7", "source_revision": null, "os": "linux", "architecture": null, "backend": "hyperv", "model": "claude-haiku-5-5", "model_version": "claude-haiku-5-5", "agent_execution": "native_agent", "agent_runtime": "claude-code", "agent_runtime_version": "2.1.310 (Claude Code)", "tool_transport": "mcp-assigned-vm", "runtime": null, "runtime_version": null, "runtime_revision": null, "sandbox_source": null, "guest_setup": null, "source_pin": { "commit": "fdf4ba96cd87d6e57588126bcc9857db05303cd7", "url": "https://github.com/phrugsa-limbunlom/deltaflow" }, "platform_details": { "os": "linux", "backend": "hyperv" }, "platform_conflicts": null, "reasoning": "low", "service_tier": null, "resources": { "cpus": 2, "memory_mib": 4096, "disk_mib": 32768 }, "operation": "Install DeltaFlow from source per README 'Install' and run the documented Usage/Examples: linear interpolant, Euler sampling, flow-matching training, and posterior sampling examples, plus the README Development pytest suite.", "operation_scope": "product operation", "hosting": null, "kind": "repo", "test_version": "readme-a0d0e27a4d0fd4c50903587087c25799260ee2578ee1953a6356df3e4fd02c35", "evidence_version": null }, "other_model": null, "other_run_id": null, "models_agree": null, "furthest_model": null, "transcript_url": "/transcripts/phrugsa-limbunlom--deltaflow-claude-haiku-5-5-1793170996249152880.txt", "report_url": "/reports/phrugsa-limbunlom--deltaflow-claude-haiku-5-5-1793170996249152880.json" }