Night market research frontier

Night Market Research Frontier

This file lists the open problems where this repository holds assets that the published state of the art does not. SOTA (state of the art) here means the best result shipped or published anywhere, not the best result in this repo. Every entry is a candidate. Nothing below is a claimed capability, and citing this file as evidence that a capability exists is an error.

Read this skill when choosing what research bet to place next, when framing an experiment, or when someone asks "what could this project contribute beyond itself?"

Ground rules

Follow these before starting any problem below.

• Everything here stays labeled open or candidate until it clears the repo evidence bar: one mechanism must explain all observations including negatives, the hypothesis must predict numbers before the run, and the generator is never its own judge. The pipeline from hunch to accepted result is night-market-research-methodology.
• Experiments are changes. They go through the same gates as any other change (night-market-change-control). This skill authorizes no shortcuts.
• When a problem produces an accepted result, write the dated synthesis in docs/research/, update the changelog, and remove or re-scope the entry here. A frontier list that never shrinks is a wish list.

Problem index

#	Problem	Primary repo asset	Status
1	Completion integrity in autonomous loops	egregore gate + herald judge + imbue verifier-integrity	Open, active campaign
2	Skill-graph governance at scale	forced-eval activation harness + ratchets	Open
3	Collective memory across context resets	ADR-0007 Discussions + memory-palace	Open, partially blocked
4	Insight-palace bridge under a hook budget	Draft spec v0.1.0 + hook infrastructure	Open, spec drafted
5	Behavioral contract attestation	ADR-0008 SLSA path + trust workflow	Open

1. Completion integrity in autonomous loops

Why current SOTA fails

Autonomous agents self-report "done." The repo's two research syntheses (docs/research/2026-07-01-the-coming-loop-agentic-harness-guardrails.md and docs/research/2026-07-01-prover-verifier-loops-formal-verification.md; note that docs/research/ is gitignored and machine-local, so these files are absent on fresh clones and the load-bearing claims are inlined here) collect the evidence: the METR randomized trial (arXiv 2507.09089) found experienced developers 19% slower with AI while believing they were faster, so self-assessment of completion is miscalibrated even for humans in the loop. On the verifier side, a green check proves spec-satisfaction, not correctness: the spec can be wrong, or the check can be hollow (a test that passes no matter what the code does). An agent that judges its own work optimizes the judge, not the work. No published harness binds "done" to gates the agent cannot fake.

This repo's specific asset

Three shipped, tested mechanisms that most agent frameworks lack:

• egregore's opt-in completion-integrity gate: completion_integrity: bool = False in plugins/egregore/scripts/config.py (commit 83281337, default off). When true, a "fix-required" quality verdict blocks the ship step and merge is held for human review regardless of auto_merge. The raw-JSON opt-in path is covered by tests (commit cd903cbf).
• herald's deterministic-first Stop-hook judge: plugins/herald/hooks/double_shot_latte.py. Deterministic verdict by default. An optional LLM second shot fires only on the single ambiguous outcome and is capped at LLM_TIMEOUT_SECONDS = 8 inside the 10s registered hook budget (commits 3d22f02a, 268cff89).
• imbue's verifier-integrity module: plugins/imbue/skills/proof-of-work/modules/verifier-integrity.md (commit 29081fda): proves the check was worth passing, distinct from proving it passed.

First three steps in this repo

1. Read the executable plan in night-market-completion-integrity-campaign. That skill owns the campaign. This entry only frames the research question.
2. Run egregore on a small manifest with the gate on (raw-JSON opt-in, completion_integrity: true) and again with it off, on the same work items. Log every quality verdict.
3. Compare false-done rates: items the ungated loop shipped that the gated loop held as fix-required, adjudicated by a human.

You have a result when

A measured false-done rate delta between gated and ungated runs on the same work items exists, with the human adjudication recorded, and the delta survives a second run. If the delta is zero or the gate holds only items a human calls genuinely done, the gate as designed is falsified: record that too. The promotion question (default-off to default-on) is open until this number exists.

2. Skill-graph governance at scale

Why current SOTA fails

This repo carries 197 registered skills (198 SKILL.md files on disk, find count 2026-07-02) against a finite skill discovery budget of about 16K characters. Skills past the budget are dropped silently (docs/quality-gates.md, Follow-on work section). The activation layer does near-keyword matching, so relevant skills fail to fire (prototypes/forced-eval/README.md). No one, here or elsewhere, has published a principled activation-quality metric: a way to say "this skill library activates the right skill X% of the time, and change Y moved that number."

This repo's specific asset

• An activation-lift measurement harness: prototypes/forced-eval/measure_activation.py (commit 5683e89b). It runs labeled prompts through claude -p with and without a forced-eval hook, counts expected Skill() events, tracks false activations on true-negative cases, and applies a paired McNemar significance test. The dataset (prototypes/forced-eval/activation_cases.json) is deliberately small. The README says to expand it before trusting the rates.
• Ratchets that already hold the graph steady: scripts/check_skill_graph_drift.py (dangling Skill() refs) and scripts/check_skill_exit_criteria_drift.py.
• A role taxonomy (entrypoint / library / hook-target) in docs/skill-integration-guide.md.
• ADR-0015 (usage-data gates before simplifying over-built skills) and the issue #574 backlog: 9 pensive review-named skills, of which at least 5 repeat the same "Approve / Approve with actions / Block" verdict scaffold (rg -l "Approve with actions" plugins/pensive/skills/*/SKILL.md matches 7 files, 2026-07-02).

First three steps in this repo

1. Expand prototypes/forced-eval/activation_cases.json with labeled positive and true-negative prompts for the pensive review skills.

2. Baseline: run the harness dry, then live.

   cd prototypes/forced-eval
   uv run python measure_activation.py            # dry run, spends nothing
   uv run python measure_activation.py --live \
       --root "$PWD/../../plugins/pensive" --repeats 3
   

3. Consolidate pensive:shell-review and pensive:makefile-review into pensive:unified-review as modules (issue #574 item 1, one PR per skill, thin command alias stubs kept), then re-run step 2.

You have a result when

A measured activation-lift delta exists for the pensive consolidation: activation rate on the labeled set before versus after, with McNemar significance, plus the discovery-budget character count saved. A result where consolidation saves budget without degrading activation is publishable. A result where activation drops is the falsification and blocks further consolidation. Candidate follow-on, unproven: turn the harness into a CI gate for any skill-count change.

3. Agent collective memory across context resets

Why current SOTA fails

Published agent-memory work centers on single-agent vector stores. Retrieval precision is rarely measured, and nothing binds memory to a team of agents whose contexts reset constantly. The failure mode is documented in this repo's own history: the abstract Stop hook that posts daily [Learning] digests read env vars Claude Code never sets and was a silent no-op for months (fixed in 1.9.14 via the shared stdin-first payload reader). Memory systems fail silently, and nobody notices until the knowledge is needed.

This repo's specific asset

• ADR-0007: GitHub Discussions as shared agent memory, written by distributed plugin hooks through leyline GraphQL wrappers. The gh discussion subcommand does not exist, so all access is gh api graphql.
• A promotion pipeline: plugins/memory-palace/skills/knowledge-intake/modules/discussion-promotion.md routes reviewed Discussions knowledge into palace storage.
• plugins/memory-palace/skills/memory-clarity-probe/SKILL.md: dual anchor questions probing whether a summary preserves task progress and information gaps across a handoff.
• The digest producer itself: plugins/abstract/hooks/post_learnings_stop.py.

Open blockers

• Retrieval precision over the Discussions corpus is unmeasured.
• The RL training path for the memory-clarity probe is blocked on logprob access (issue #553, open as of 2026-07-02).

First three steps in this repo

1. Build a labeled retrieval set: sample 30 to 50 existing [Learning] and [Knowledge] discussions via gh api graphql, and for each write 1 to 2 queries a future session would plausibly ask.
2. Measure memory-palace:knowledge-locator precision and recall against that set. Record the numbers in a dated docs/research/ synthesis.
3. Instrument the promotion pipeline: log how often promoted knowledge is retrieved within 30 days, versus knowledge left in Discussions.

You have a result when

Precision and recall numbers exist for a labeled query set, and one curation change (for example, promoting versus not promoting a batch) produces a predicted, then measured, retrieval delta. Issue #553 unblocks a stronger result (an RL-trained clarity probe), but the retrieval measurement does not wait on it.

4. Insight-palace bridge under a hard hook budget

Why current SOTA fails

Plugin ecosystems either share a runtime registry (tight coupling) or do not exchange data at all. ADR-0001 forbids a shared registry here: plugins detect each other via the filesystem and degrade gracefully. Moving structured findings between two isolated plugins inside a Stop hook's hard latency budget, with graceful failure when the peer plugin is absent, is an unsolved composition problem, and hook-budget overruns are a known repo failure class (herald's LLM timeout once exceeded its registered budget and the harness killed the hook with no verdict at all).

This repo's specific asset

A drafted, unimplemented specification: docs/specification.md (Insight-Palace Bridge, v0.1.0, Draft, 2026-04-13), with docs/project-brief.md and docs/implementation-plan.md. Key verified constraints:

• The Stop hook budget is 8.5s: _BUDGET_SECONDS = 8.5 in plugins/abstract/hooks/post_learnings_stop.py, leaving headroom inside the 10s hook timeout.
• AC-3.1: ingestion of up to 10 findings completes in under 500ms.
• AC-3.2: the bridge checks remaining budget and skips if less than 1s remains. AC-3.4: it never raises to the caller.
• Cross-plugin absence is handled by an ImportError guard (_HAS_INSIGHT_ENGINE): with memory-palace or the insight engine missing, the bridge silently does nothing.

Caution: the brief, specification, and implementation plan under docs/ are overwritten per feature cycle. Confirm the spec on disk is still the insight-palace bridge before building against it.

First three steps in this repo

1. Read docs/specification.md and docs/implementation-plan.md end to end, and confirm the Draft status and version are unchanged.
2. Implement the bridge script per TR-1 with the _HAS_INSIGHT_ENGINE guard and the remaining-budget check, tests first (Iron Law applies).
3. Add a timing test proving AC-3.1 (10 findings under 500ms) and a test proving the ImportError path is a silent no-op, using a sys.meta_path import blocker as the existing hook regression tests do.

You have a result when

The bridge is merged with both tests green, a benchmark artifact shows 10-finding ingestion under 500ms on CI hardware, and the spec's status line moves from Draft. Falsification: if the 500ms budget cannot be met without dropping findings, that is a spec revision, not a reason to remove the budget check.

5. Behavioral contract attestation for plugin marketplaces

Why current SOTA fails

Supply-chain attestation (SLSA provenance, signed via Sigstore) proves which bytes came from which workflow. It does not prove what the artifact does. ADR-0008 states the gap directly: there is no mechanism to prove that a plugin's behavioral contract holds. A marketplace can today verify a plugin is unmodified and still ship a plugin whose hooks do something other than what its README claims. SLSA is the state of the art for artifacts. Behavior verification has no SOTA to beat, only a vacancy.

This repo's specific asset

• ADR-0008 (Accepted, self-superseded 2026-03-15: the ERC-8004 blockchain path was dropped for cost in favor of GitHub Attestations/SLSA).
• A live attestation pipeline: .github/workflows/trust-attestation.yml runs make test on master pushes and produces a signed SLSA attestation of trust-report.json.
• A consumer: the leyline:verify-plugin command (plugins/leyline/commands/verify-plugin.md) checks a plugin's attestation history.

First three steps in this repo

1. Define what trust-report.json would need to assert for behavior, not provenance: candidate schema is per-hook contract tests (input payload, expected verdict/exit) whose pass results are attested.
2. Add one behavioral contract test to the trust report for a single hook (herald's Stop-hook judge is the best-instrumented candidate) and attest it through the existing workflow.
3. Extend leyline:verify-plugin to compare the attested behavioral claims against the plugin currently on disk and flag divergence.

You have a result when

leyline:verify-plugin distinguishes, in a test, a plugin whose attested behavior diverged from an unmodified one. Candidate and unproven beyond that: whether behavioral attestation generalizes past hooks (skills and agents are prose, with no test harness for their behavior yet). Label any generalization claim open until one exists.

What beyond-SOTA means here

Inferred from the project's own research docs, and labeled as inference: the ambition is harness-level guardrails that keep autonomous loops honest and legible. The five problems above are one thread: gates the agent cannot fake (1), a skill library whose activation is measured rather than hoped (2), memory that survives resets and proves its retrieval (3), cross-plugin composition under hard budgets (4), and trust signals that cover behavior, not bytes (5). Advancing any one of them past its milestone is a contribution the wider agent-tooling field does not yet have.

When NOT to use

• Executing the completion-integrity work: use night-market-completion-integrity-campaign, which owns the runnable plan. This entry only frames the research question.
• Running the hunch-to-result process for any experiment: use night-market-research-methodology.
• Looking up what already failed and was settled: use night-market-failure-archaeology. Do not reopen settled battles as "research."
• Day-to-day test/lint/release commands: use night-market-operations.
• Understanding the invariants an experiment must not break: use night-market-architecture-contract.

Exit Criteria

• A specific problem number (1 to 5) was chosen and its listed first three steps were either started as written or a documented deviation exists in the work log or PR description.
• Any claimed result names its "you have a result when" milestone and shows the milestone's check passing (numbers, test output, or merged artifact).
• No statement from this file was cited as evidence of a shipped capability, and every borrowed claim kept its open/candidate label.
• The experiment's changes passed the normal gates (failing test first for plugin Python, pre-commit clean, no bypass flags).
• If a result was accepted, a dated synthesis exists in docs/research/ and this file's entry was updated or removed.

Provenance and maintenance

Compiled 2026-07-02 against repo v1.9.15 (branch discussions-fix-1.9.14). Volatile facts and how to re-verify them:

• Skill count (198 SKILL.md files, 2026-07-02): find plugins -name SKILL.md | wc -l
• egregore gate default (off, 2026-07-02): rg -n "completion_integrity" plugins/egregore/scripts/config.py
• herald LLM timeout (8s, 2026-07-02): rg -n "LLM_TIMEOUT_SECONDS" plugins/herald/hooks/double_shot_latte.py
• Insight-palace spec still current (Draft v0.1.0, 2026-04-13): head -5 docs/specification.md
• Stop-hook budget (8.5s): rg -n "_BUDGET_SECONDS" plugins/abstract/hooks/post_learnings_stop.py
• Issue states (#574 open, #553 open, 2026-07-02): gh issue view 574 --json state -q .state (same for 553)
• Pensive verdict-scaffold duplication (7 files, 2026-07-02): rg -l "Approve with actions" plugins/pensive/skills/*/SKILL.md | wc -l
• Discovery-budget note: rg -n "16K characters" docs/quality-gates.md
• Commits cited: 83281337, cd903cbf, 29081fda, 3d22f02a, 268cff89, 5683e89b. Re-verify with git log --oneline -1 <hash>.

Unverified in this compilation: the exact 16K-character discovery budget figure is the repo's own estimate ("about 16K characters" in docs/quality-gates.md), not an upstream-documented limit. The claim that no published activation-quality metric exists is a literature-absence claim as of 2026-07-02. Re-check before publishing externally.