BUILD_LOG.md

SchemaShift Build Log

Running log of everything built, tested, trained, and deployed. Append-only — never delete.

Team Tripod: Yashash Sheshagiri (lead), Gajanand V Dhayagode, Likith B S Event: Meta × HF × PyTorch OpenEnv Hackathon 2026 · Round 2 · Bangalore · April 25-26

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PHASES COMPLETED

Phase 0 — Project Scaffolding

• Date: Tuesday April 21, 2026 (evening)
• Commit: d4ab0f1
• Tests: 0 (scaffolding only — no logic yet)
• Time: ~30 min
• Notes: 29 files created. Python venv built. Deps installed cleanly (fastapi 0.136, pydantic 2.13, openai 2.32, openenv-core 0.2.3, gradio 6.13). Repo pushed to https://github.com/Yashash4/SchemaShift.

Phase 1 — Pydantic Models

• Date: Tuesday April 21, 2026
• Commit: 8cdb02b
• Tests: 6 passing (0.22s runtime)
• Line counts: models.py = 135 lines, test_models.py = ~120 lines
• Time: ~20 min
• Judgment calls:
  1. 12 classes + 2 Literal type aliases (not 13 classes as prompt suggested)
• Notes: Spec code copied verbatim. No deviations.

Phase 2 — Mail Tool

• Date: Tuesday April 21, 2026
• Commit: 8410ff1
• Tests: 7 new (13 total), 0.16s runtime
• Line counts: tools/base.py = 60, tools/mail.py = 121, tests/test_mail.py = 149
• Time: ~90 min
• Judgment calls:
  1. Absolute imports (from models import) over relative imports — matches Phase 1 convention
  2. get_schema() returns {} for unknown endpoints post-deprecation
  3. 501 fallback in BaseTool.call for schema-without-handler (dev-time safety)
  4. messages.send is deep-copy rename of send_message after deprecation drift
  5. Pagination tokens: "tok_xyz" / "cur_abc123" when >10 results, None otherwise
• Notes: 3 endpoints (list_messages, send_message, get_message), 3 drifts (field_rename, endpoint_deprecation, new_required_param).

Phase 3 — Calendar Tool + DriftInjector

• Date: Tuesday April 21, 2026
• Commit: 27dd958
• Tests: 10 new (23 total), 0.24s runtime
• Line counts: tools/calendar.py = 164, drift.py = 28, tests/test_calendar.py = 182, tests/test_drift.py = 122
• Time: ~100 min
• Judgment calls:
  1. Overlap predicate for list_events (inclusive both sides, ISO string sort)
  2. _create_event handles both attendees and participants gracefully
  3. _update_event accepts both attendees and participants (no second drift handler needed)
  4. Cancelled events remain in storage (matches Google/Outlook behavior)
  5. _fired sentinel in event.details (underscore prefix avoids collision)
  6. rate_limit_tightening chosen for unknown-drift test (semantically meaningful)
• Notes: 4 endpoints (list/create/update/delete_event), 2 drifts (field_rename attendees→participants, tool_removal delete_event). DriftInjector is stateless.

Phase 4 — CRM Tool + Scenarios E1/E2/E3

• Date: Tuesday April 21, 2026
• Commit: bc8f184
• Tests: 11 new (34 total), 0.24s runtime
• Line counts: tools/crm.py = 213, scenarios.py = 124, tests/test_crm.py = 142, tests/test_scenarios.py = 65
• Time: ~100 min
• Judgment calls:
  1. Strict param validation in BaseTool.call — rejects unknown params with 400 "Unknown params" (matches Stripe/Google API behavior)
  2. Internal storage stays customer_email; projection on read via _project_contact
  3. search_contacts() with no filters returns ALL contacts (REST convention)
  4. Rate-limit counter resets on drift fire (pre-drift calls don't count against post-drift budget)
  5. Drift-order commutativity for contacts.patch (either order produces same final state)
  6. E2 task description doesn't disclose deprecation (agent must discover via 410 response)
  7. E3 drift fires at step 2 (gives agent one clean baseline call)
  8. DriftEvent.details populated with hints (from, to) for grader/scenario auditing
• Notes: 4 endpoints (search/get/create/update_contact), 3 drifts (tool-wide field_rename, rate_limit_tightening, endpoint_deprecation update→contacts.patch).

Phase 5 — Composable Grader + Dense Step Shaping

• Date: Tuesday April 21, 2026
• Commit: 46b8c56
• Tests: 11 new (45 total), 0.25s runtime
• Line counts: graders.py = 267, tests/test_graders.py = 224
• Time: ~150 min
• Judgment calls:
  1. Binary threshold 0.95 (not 1.0) — floating point safety
  2. AdaptationRubric iterates all fired drifts (TODO: revisit for Phase 11 M-scenarios with multi-drift-per-tool)
  3. _final_state_acceptable lenient mid-episode, strict at terminal — preserves training signal
  4. compute_step_shaping checks not d.detected_by_agent — anti-farming guard
  5. Dumb-retry penalty requires same tool AND same endpoint
  6. assert for weight-sum invariant (dev-time, not runtime)
  7. Rubric names coupled to RewardBreakdown field names (intentional reward contract)
  8. Bonus test asserts step_shaping = 0 in grader output (env fills it, not grader)
• Notes: 4 rubrics (Completion, DriftDetection, Adaptation, Efficiency) + 2 gates (catastrophic, correct_final) + WeightedSum + Gate + compute_step_shaping + build_grader.

Phase 6 — Environment Scheduler

• Date: Tuesday April 21, 2026
• Commit: c618a61
• Tests: 8 new (53 total), 0.29s runtime
• Line counts: server/environment.py = 363, tests/test_environment.py = 242
• Time: ~120 min
• E1 FULL-EPISODE BENCHMARK:
  • shaped_total = 0.934375 (expected ~0.93, exact match on weighted sum)
  • task_completion = 1.0
  • drift_detection = 1.0
  • adaptation_quality = 1.0
  • efficiency = 0.5625 (used 7 of 8 max steps)
  • catastrophic_gate = 1.0, correct_final_gate = 1.0
  • binary = 1.0
  • cumulative_reward = 4.2975 across 7 steps
  • Dense shaping fired correctly: +0.10 at step 4 (inspect-after-failure), +0.15 at step 6 (drift report)
• Judgment calls:
  1. Shaping-before-marking ordering: dispatch→compute_step_shaping→mark detected→grader
  2. tokens_used clamped to non-negative with max(0, remaining - tokens_used)
  3. Lazy tool imports in _instantiate_tool — stretch tools don't break core
  4. deepcopy(drift_plan) on reset — prevents state leak across episodes
  5. Observation history windowed to last 5 steps for LLM prompt budget
  6. Calendar body parser handles both attendees (strings) and participants (dicts)
  7. _completion_summary stored with underscore prefix (no GT key collision)
  8. E2 three-recipient check tracked in _update_agent_state
  9. done flag set AFTER dispatch but BEFORE grader (enables strict gate at terminal)
  10. Dispatch returns None for report_drift and complete_task (no tool response)
• Notes: Round 1 _engine is None bug pattern explicitly prevented via RuntimeError in step() when state is None.

Phase 7 — FastAPI Server + Docker

• Date: Tuesday April 21, 2026 (late evening)
• Commit: cb33205
• Tests: 10 new (63 total), 0.94s runtime
• Line counts: server/app.py = 120, tests/test_server.py = 129, Dockerfile = 14, openenv.yaml = 14
• Time: ~60 min
• Live server smoke tests: uvicorn on 127.0.0.1:7860 verified /health, /tasks (count=3), /reset (E1 → step=0, tools=['mail', 'calendar']) all 200 OK
• pip install -e . confirmation: Editable wheel built successfully. Docker container will resolve absolute imports correctly.
• Judgment calls:
  1. Removed root __init__.py — conflict with py-modules in pyproject
  2. py-modules + packages split in pyproject (preserves from models import convention)
  3. Dropped unused imports from spec's server code
  4. TestClient monkeypatch fixture — fresh env per test prevents cross-contamination
  5. /step catches RuntimeError → 400 not 500 (client bug not server bug)
  6. Read-only endpoints (/state, /grader) don't auto-reset
  7. /tasks trims description to 120 chars (lightweight listing)
  8. No CORS middleware yet (defer to Phase 10 if needed)
  9. Hardcoded version "0.1.0" in two places (pragmatic duplication)
  10. urllib.request for smoke test (Windows-safe, no curl dependency)
• Notes: Endpoints: /, /health, /reset, /step, /state, /tasks, /grader. Port 7860 for HF Spaces compatibility.

Phase 8 — Env Client + Training Skeleton

• Date: Tuesday April 21, 2026 (late evening)
• Commit: 2464e9e
• Tests: 4 new (67 total), 1.52s runtime
• Line counts: client.py = 91, training/grpo_smoke.py = 143, training/grpo_kaggle.ipynb = 10 cells (1 markdown + 9 code), tests/test_client.py = 70
• Smoke test result: Step 4 step_shaping = 0.1000 ✅ EXACTLY as expected (dense shaping preserved across HTTP roundtrip: env → JSON serialize → HTTP → JSON deserialize → client → caller)
• Time: ~90 min
• Judgment calls:
  1. Brace-balanced JSON extractor in parse_completion_to_actions (depth-counting; handles arbitrarily nested JSON, unlike flat regex which would miss call_tool.tool_call.params structures)
  2. Mocked httpx tests over live TestClient (faster, no real network, no port conflicts; live integration covered by grpo_smoke.py)
  3. Notebook uses explicit Cell 4 / Cell 5 / Cell 6 separation (variant block uncommenting is cleaner than runtime flags — one account owner toggles exactly one block)
  4. Smoke test returns exit code 2 (not 1) when step_shaping is wrong, distinguishing from "server unreachable" exit 1
  5. Model name in Cell 4 left as Qwen 2.5 1.5B Instruct (Account 3 Coder ablation is documented as a manual edit, not conditional code)
  6. Procedural drift scheduler lives in its own Cell 6 (Account 3 rebuilds dataset each ~25 steps without editing reward_fn)
  7. GRPO hub_model_id uses $HF_USERNAME env var fallback to Yashash4 (each account member pushes to their own HF namespace — prevents checkpoint collisions)
  8. Notebook Cell 3 uses git clone || git pull (idempotent on warm Kaggle sessions)
  9. Client tests mock _client.get/post directly, not httpx.Client (surgical, exercises same code path runtime hits)
  10. Deferred live HTTP client tests to Phase 10 (grpo_smoke.py is the live integration check — passed cleanly)
• Notes: This is the bridge to training. After this phase, Kaggle can load Qwen 1.5B, generate action JSON, submit via HTTP, receive shaped rewards, and feed GRPO. The 0.1000 step_shaping on smoke Step 4 is the "signal is alive" proof — the most important number from Phase 8.

Phase 9 — Baseline Eval Harness

• Date: Tuesday April 21, 2026 (late night)
• Commit: 4d2f869
• Tests: 6 new (73 total), 1.53s runtime
• Line counts: eval.py = 674, tests/test_eval.py = 132
• Time: ~2 hours
• Live eval discriminability result:
  • naive_heuristic: 0.000 shaped, 0.235 cumulative, 0% binary
  • policy_aware_heuristic: 0.348 shaped, 1.284 cumulative, 66.67% binary
  • GAP: 0.348 shaped (threshold was 0.2 → PASS)
  • Binary rate gap: 66.67 percentage points
• Per-scenario behavior:
  • E1: policy_aware finishes in 3 steps, beats drift at step 3, binary=1
  • E2: policy_aware hits unavoidable drift at step 1, recovers but only sends 1/3 emails, binary=0 — intentional ceiling for rule-based agents
  • E3: policy_aware search+update completes cleanly, binary=1
• Judgment calls:
  1. Retry guard to prevent infinite loop (caught upfront)
  2. Task-progress awareness via obs.known_state (prevents duplicate tool calls)
  3. CRM search→update two-stage flow with company name capture
  4. _adapt_endpoint handles mail messages.send and crm contacts.patch
  5. Email regex bug fix (r"[\w.+-]+@[\w-]+(?:\.[\w-]+)+" — can't swallow trailing period; caught via debug, would have broken E3 silently)
  6. Added cumulative_reward to EpisodeResult output (captures dense shaping even when gate zeros terminal)
  7. Strict-validation-aware _adapt_params (strips keys not in current schema)
  8. LLM agents deferred to Phase 10+ (API keys not ready)
• Notes: Discriminability proves env rewards the correct meta-skill and isn't gameable. E2's design feature (unreachable for rule-based agents) is critical for RL training story — demonstrates the policy ceiling that a trained agent must exceed.

Phase 10 — HF Space Deploy

• Date: Tuesday April 21, 2026 (late night)
• Commit: fc82aca (merge with HF initial commit)
• Tests: 4 new deploy smoke tests (73 local + 4 conditional = 77 total)
• Line counts: README.md (HF frontmatter + body), DEPLOY.md, tests/test_deploy_smoke.py
• Time: < 30 minutes (fastest phase so far)
• Deploy timing: HF Docker base cached → APP_STARTING at t=0s, RUNNING at t=15s, /health 200 immediately
• Live URLs:
  • HF Space: https://yashash045-schemashift.hf.space
  • HF Space management: https://huggingface.co/spaces/yashash045/schemashift
  • GitHub: https://github.com/Yashash4/SchemaShift
• CRITICAL VERIFICATION — Step 4 step_shaping on production:
  • Local Phase 8 smoke: 0.1000 exact
  • Production Phase 10 smoke: 0.1000 exact — dense shaping preserved through full prod HTTP path
• Heuristic eval on production (matches local exactly):
  • naive: 0.000 shaped, 0.235 cumulative, 0% binary
  • policy_aware: 0.348 shaped, 1.284 cumulative, 66.67% binary
  • Gap: +0.348 shaped / +66.67pp binary (matches local to 3 decimal places)
• Deploy smoke test suite: 4/4 passing (test_deployed_health, test_deployed_tasks_list, test_deployed_reset_and_step, test_deployed_step_shaping_fires) — 6.32s runtime
• Judgment calls:
  1. Used existing HF creds (yashash045) found at ~/.cache/huggingface/token instead of re-auth
  2. GitHub Yashash4 vs HF yashash045 — different handles, both remotes working
  3. Merge HF initial commit with -X ours strategy, not force-push (audit trail preserved)
  4. Deploy smoke tests skippable via SCHEMASHIFT_DEPLOY_URL env var
  5. Monitor tool for build-status polling (context-efficient)
  6. Tokenized URL for git push (portable across Win/Unix)
  7. Dual remote setup — both origin + space pushed at fc82aca
  8. Deploy took < 1 min end-to-end; redeployments will be < 30s (base image cached)
• Notes: Kaggle training notebooks can now set SCHEMASHIFT_URL=https://yashash045-schemashift.hf.space for Phase 13. README.md has HF frontmatter with patronus+scaler sub-theme tags for judge discoverability.

Phase 11 — Medium Scenarios M1/M2/M3

• Date: Wednesday April 22, 2026 (early morning)
• Commits: 7828dcd (Phase 11 core) + 0fd93c4 (deploy smoke count fixup)
• Tests: 5 new (78 local pass + 4 deploy skip = 82 collected), 4/4 deploy smoke pass on prod
• Line counts: scenarios.py 124→283 (+159), server/environment.py 363→390 (+27), tests/test_scenarios.py 65→95 (+30), tests/test_graders.py 224→295 (+71)
• Time: ~2 hours
• M-tier discriminability result (policy_aware_heuristic, 3 seeds):
  • M1_customer_escalation: shaped=0.000, cumul=2.510, binary=0%
  • M2_weekly_report: shaped=0.000, cumul=3.013, binary=0%
  • M3_event_cleanup: shaped=0.000, cumul=0.441, binary=0%
  • M-tier overall: shaped=0.000, cumul=1.988 (HIGHER than E-tier's 1.284), binary=0%
• Interpretation: Gate-zeroed terminal rewards + higher cumulative = dense shaping fires correctly during multi-drift adaptation but rule-based agent cannot complete M-tier tasks. This is the designed ceiling — M-tier requires planning ahead across 10-15 steps with 2 drifts per episode, which no keyword-triggered heuristic can do. M-tier is the "requires RL" tier.
• E-tier regression check: policy_aware on E1/E2/E3 still returns 0.348 shaped / 66.67% binary — identical to Phases 9/10. Env extensions didn't regress easy scenarios.
• AdaptationRubric multi-drift test (judgment call #2 from Phase 5 resolved):
  • Test constructed synthetic M3 state: 2 calendar drifts fired, 1 adaptation opportunity succeeded
  • Score: 0.5 (1/2 opportunities adapted) — partial credit for partial adaptation
  • Decision: NOT modified. Conservative partial-credit rubric + dense step_shaping (+0.20 for successful retry) together provide clean decomposable training signal. Change only if Phase 13 training shows convergence issues.
• Production verification: HF Space /tasks now returns 6 scenarios. Zero-downtime redeploy. M-tier prod eval matches local to 3 decimals.
• Judgment calls:
  1. Extended calendar.last_event_has_both_attendees to OR-match priya+alex AND bob+alex (shared GT key across E1 and M1)
  2. Added 3 new subject-contains checks (priority_support, weekly, calendar_updated) following existing pattern
  3. Dynamic calendar.evt_{X}_status tracking on update_event status param — good pattern for entity-specific GT
  4. calendar.events_count_new_friday_wrapup counter — case-insensitive title substring
  5. Renamed test_all_three_scenarios_present → test_all_scenarios_present (6 scenarios now)
  6. Updated test_tasks_endpoint and test_deployed_tasks_list count assertions 3→6
  7. AdaptationRubric NOT modified — partial-credit behavior is correct (prevents double-counting, preserves signal)
  8. Heuristic (eval.py) NOT modified — M-tier shaped=0 is the designed ceiling, not a heuristic bug to fix
• Notes: After Phase 11, SCHEMASHIFT_URL/tasks returns 6 scenarios (3 easy + 3 medium). Phase 13 training uses all 6 as training data. H-tier (hard) scenarios deferred to stretch.

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PHASES REMAINING

• Phase 11 — Medium scenarios (M1/M2/M3) ✅ commit 7828dcd + 0fd93c4
• Phase 12 — Insurance video recording (60s core cut + 2min full)
• Phase 13 — Kaggle training runs (Stage 1 single account → Stage 2 parallelize)
• Phase 14 — Pitch + blog + video
• Phase 15 — Onsite Saturday-Sunday

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TRAINING RUNS / EVAL RUNS — see TRAINING_LOG.md

All training runs (per-checkpoint evals, reward curves, config snapshots, iteration history) are logged in TRAINING_LOG.md — a dedicated append-only file with 7 structured sections. This separation keeps BUILD_LOG focused on build/phase tracking. For Phase 9 eval results, Phase 13 training data, and head-to-head comparison tables, see TRAINING_LOG.md.

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DEPLOYMENTS

(Will be populated during Phase 10+.)

Deploy v[N] — [Date]

• Target: HF Space / local / Kaggle
• Commit:
• URL:
• Health check:
• Config secrets set:
• Notes:

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DECISIONS LOG (strategic, append-only)

Tuesday ~9 PM — Domain lock-in

• Decision: Schema Drift Adaptation Arena (SaaS Admin Workflows)
• Rationale: Direct Patronus sub-theme hit; Scaler secondary; no competitor finalist has it; authentic to Yashash's APEX OS/VisionX experience; cleanest before/after RL story

Tuesday ~10 PM — Staged Kaggle approach (v2.3)

• Decision: Run ONE Kaggle account (Gajanand/Account 2, main config) first. Parallelize only if Stage 1 converges.
• Rationale: If reward function or parser has a bug, 3 simultaneous accounts waste 3× GPU quota. Debug once on one account. Main config = primary pitch claim, so prove that first.

Tuesday ~10 PM — Qwen 2.5 Coder 1.5B ablation on Account 3 (v2.2)

• Decision: Account 3 uses Coder-1.5B instead of same model + curriculum only.
• Rationale: More informative ablation — tests whether code-pretraining helps schema-adaptation learning. Gives blog/pitch a secondary result regardless of outcome.

Tuesday ~10 PM — Two videos in Phase 12 (v2.1)

• Decision: Produce 60-second core cut + 2-minute full version, not one 90-second video.
• Rationale: 60s fits pitch timing with hook+close room. 2min fits HF blog context depth. Recording once (Thursday), using twice.

Tuesday ~10 PM — One-sentence pitch opener (v2.1)

• Decision: Open README/pitch/video with: "SchemaShift teaches agents to recover when the tool schema changes under them."
• Rationale: Gallery winners (Kube SRE Gym, GAIA) lead with one-sentence claims. Judges remember one clean idea, not four themes.

Tuesday ~10 PM — Procedural drift scheduler on Account 3 (v2.1)

• Decision: Account 3 variant adds ~30 LOC scheduler that rotates drift steps and adds secondary drift after step 150.
• Rationale: Kube SRE Gym uses Claude-powered adversarial designer; we achieve similar curriculum story without API credit burn.

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REJECTED IDEAS (do not revive — posterity record)

Validator / oversight agent — rejected Tuesday

• Why rejected: Would break 63 passing tests, adds scope at wrong phase, theme-benefit marginal (multi-agent claim already defensible via scripted tool servers)

Uncertainty injection across failure types — rejected Tuesday

• Why rejected: Dilutes pitch (becomes "uncertainty" not "schema drift"), risks GRPO convergence by introducing probabilistic reward, already solved by dense shaping + policy-aware heuristic baseline proving non-gameability

Claude-powered adversarial drift designer — rejected Tuesday

• Why rejected: Time sink, API credit burn, provides marginal benefit over procedural scheduler

Pivot to "decision-making under uncertainty" framing — rejected Tuesday

• Why rejected: Breaks Patronus sub-theme fit, muddies demo story, opens scope unnecessarily

Qwen3 family migration (primary training) — rejected Tuesday

• Why rejected: Newer but less stable, prompt-behavior churn, migration risk for 48-hour build. Stick with Qwen 2.5 1.5B.

3B+ primary model — rejected Tuesday

• Why rejected: Slow iteration (2x fewer experiments per Kaggle hour), tight T4 memory, doesn't improve pitch. "1.5B beats GPT-4o-mini" is a stronger claim than "3B beats GPT-4o-mini".

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OPEN QUESTIONS / TODOS

• Phase 8: verify step_shaping survives HTTP serialization roundtrip
• Phase 9: do baseline LLMs hit at least 0.30 on drifted E1? (if too high, env is too easy)
• Phase 10: deploy to HF Space, verify remote /health works
• Phase 11: when adding M1/M2/M3, verify AdaptationRubric denominator still makes sense (flagged in Phase 5 judgment call #2)
• Phase 13 Stage 1: go/no-go decision at 20 steps — document the reward curve image
• Phase 14: draft strong-claim and softer-claim versions of pitch, pick based on training results
• Phase 15 onsite: bring a backup video file on USB in case HF Space is slow to load
• Phase 13 Stage 1 Eval: when evaluating trained checkpoint, include M-tier in the scenarios list. M-tier is where the "beats GPT-4o-mini" claim lives.
• Pitch Phase 14: frame heuristic brittleness on M-tier ("can't parse 'schedule'/'check-in call' as calendar intent") as evidence that env rewards real language understanding, not regex matching.

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ONSITE LOGISTICS

Event: Saturday-Sunday April 25-26, 2026 · SST Campus Bangalore Travel: (add details) Accommodation: (add details) Equipment to bring:

• 2 laptops (primary + backup)
• USB with video files + repo backup
• HDMI adapter for presentation
• Chargers + extension cord
• Both phones with hotspot capability (in case venue wifi fails)

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