# Project Status

last_reviewed: 2026-07-27
baseline_before_update: `5373d56`
branch: `main`
remote: `https://git.k1412.top/wuyang/agent.git`

## Purpose

The repository began as a durable Agent knowledge base and now has two layers:

1. a collection layer for papers, Beijing jobs, and company Agent work;
2. an experimental knowledge interface and research layer intended to turn the collection into evidence-backed understanding.

The long-term product is not “a place to browse papers.” It should help the user retain claims, connect them into a personal model, test that model, and use it to reason about unfamiliar Agent problems.

## Current Snapshot

The deterministic index currently contains 1,140 items:

| Collection | Count | State |
| --- | ---: | --- |
| papers | 1,125 | 1,110 queued, 15 skimmed |
| jobs | 6 | 4 active, 2 unknown |
| industry | 9 | 8 analyzed, 1 queued |

The 2026-07-08 expanded arXiv run saw 1,506 candidates, promoted 970 high-recall candidates,
and retained 536 reserve candidates. The 2026-07-27 incremental refresh covered July 9-27, saw
249 unique candidates, selected 157 high-relevance records, skipped 6 existing papers, and added
151 paper items. The newest submission returned was 2026-07-24. These are collection counts, not
quality or reading-completion counts.

The memory topic has two intentionally different counts:

- 336 indexed items now have the broad `memory` topic, including non-paper streams;
- 293 local paper items were resolved for the dedicated memory audit.

The memory research snapshot contains:

| Stage | Count |
| --- | ---: |
| paginated arXiv candidates | 516 |
| lexical title-central upper bound | 379 |
| recent-six-month title-central candidates | 292 |
| PDFs downloaded and text-located during the audit | 32 |
| papers promoted into the evidence ledger | 23 |

The strongest current conclusion is that recent progress is concentrated in better diagnosis/evaluation and task-specific execution-state memory, not a universal memory architecture. Read `research/memory/findings.md` before making memory design claims.

The Agent completion/verification research snapshot contains:

| Stage | Count |
| --- | ---: |
| full-text problem-driven pool | 40 |
| papers promoted into the evidence ledger | 37 |
| historical context only | 3 |

Its strongest conclusion is that Agent completion is an evidence contract over intended effects,
forbidden effects, required procedure, and remaining uncertainty. State checks, trace rules, learned
judges, and humans have different jobs; none is a universal oracle. Verification and recovery are
separate capabilities, and recovery must distinguish detection, localization, routing, restoration,
and re-verification. Read `research/completion-verification/findings.md` before extending the Agent
evaluation contract.

## Artifact Map

| Area | Source of truth | What it contains |
| --- | --- | --- |
| entry/navigation | `README.md` | human-facing repository map |
| AI continuation | `AGENTS.md` | read order, contracts, commands |
| stable concepts | `docs/00-05*.md`, `docs/99-glossary.md` | knowledge map, principles, architecture, practice, evaluation, collection |
| current state | `docs/06-project-status.md` | counts, runtime, artifact inventory |
| exploration history | `docs/07-exploration-history.md` | accepted and rejected routes |
| continuation plan | `docs/08-next-work.md` | priorities and definition of done |
| jobs | `jobs/` | Beijing-first rolling six-month JD stream |
| papers | `papers/` | paper metadata, queue, notes, corpus summary |
| industry | `industry/` | official reports, blogs, products, benchmarks |
| collection runs | `collection-runs/` | queries, scope, counts, method notes |
| collection tools | `tools/collection/` | item creation, arXiv collection, promotion, indexing, URL checks |
| research | `research/memory/`, `research/completion-verification/` | field findings and evidence ledgers |
| Agent evaluation | `research/evaluation/` | executable design contract for task suites, validators, paired comparisons, and release gates |
| evaluation data/tools | `data/evaluation/`, `tools/evaluation/` | versioned task contracts and JSONL result scorer |
| evaluation report | `evaluation-site/` | Chinese interactive explanation of the contract, decision gates, tasks, and artifacts |
| research data | `data/research/` | committed corpus, screening, landscape and CSV ledger |
| research tools | `tools/research/` | paginated search, abstract fetch, screening, classification, landscape build |
| experiments | `experiments/knowledge-compilation/` | failed pilot and completed full-text research audits |
| learning prototype | `learning/agent-memory/` | preserved failed 12-paper learning module |
| web application | `web/` | Atlas, search agent, paper reader/chat, Ollama actions |
| project integrity | `tools/check_project.py` | offline structural and data checks |

`projects/` and `references/` are still mostly scaffolding. They do not yet contain substantive project or general-reference catalogs.

## Data Lineage

```text
official/web/arXiv sources
  -> jobs|papers|industry/items/*.md
  -> tools/collection/build_index.py
  -> data/index.json + data/summary.json
  -> web/app.py
  -> Atlas / search / paper reading UI
```

The dedicated memory audit is separate:

```text
paginated arXiv queries + local memory-tagged papers
  -> data/research/* corpus snapshots
  -> model-assisted triage + deterministic statistics
  -> selected full-text experiment checks
  -> research/memory/evidence-ledger.md
  -> research/memory/findings.md
```

Do not merge these two lines silently. The first is a broad browsing corpus; the second is an evidence audit with explicit boundaries.

The Agent completion audit follows the same evidence discipline without a model-assisted screening
stage:

```text
problem-driven arXiv candidates
  -> 40 downloaded and text-located full papers
  -> 37 audited experiment/ablation/limitation records
  -> research/completion-verification/evidence-ledger.md
  -> research/completion-verification/findings.md
```

The PDF and extracted-text working set was temporary. The committed evidence ledger preserves source
IDs, key numbers, claim boundaries, and exclusions so another researcher can refetch the sources.

The Agent evaluation design follows a third line:

```text
zk-data-agent runtime/events + Zero decision-loop constraints
  -> versioned task contracts
  -> isolated repeated baseline/variant runs
  -> independent validators + trace rules
  -> JSONL result records
  -> gain/regression/safety decision
```

Only the task contracts and scorer exist in this repository. The reusable runner, fixtures, validators,
and real-model comparison still need to be implemented in `zk-data-agent`. That implementation is
intentionally paused while the research findings are reviewed. Before implementation, task contracts
need separate outcome/invariant/procedure/semantic-residual layers, non-binary
`ambiguous/unverifiable` states, and explicit recovery-stage fields.

`evaluation-site/` visualizes this contract but does not add evidence. Its adjustable decision values are
explicitly labeled as a method simulation, not a real benchmark.

## Web Application

Capabilities currently implemented:

- topic Atlas with ECharts and SVG fallback;
- routes, timeline, topic focus and paper filtering;
- desktop/mobile responsive reader mode;
- local keyword search plus an Agent search loop;
- Chinese query interpretation and English keyword expansion;
- asynchronous search activity, aggregate statistics and AI brief;
- follow-up questions on a search result;
- per-paper abstract fetch, summary, translation, deep analysis and chat;
- single-concurrency Ollama access and local caches.

Run it with:

```bash
web/manage.sh start
web/manage.sh status
```

Expected addresses:

```text
local/Tailscale: http://100.114.68.27:18080
public mapping:  https://lab.k1412.top/
health:          /api/health
```

At the beginning of the 2026-07-12 handoff audit, no process was listening on 18080 and the public domain returned 502. The handoff added and enabled a user-level systemd service through `web/manage.sh`;
local and public health then returned 200. Runtime status is external state and must always be checked
live. After a corpus update, restart the service because the index is loaded at process startup.
On 2026-07-27 both the Tailscale endpoint and `https://lab.k1412.top/api/health` returned the refreshed
1,125-paper count with `ollama_ready: true`.

The live Ollama inventory checked on 2026-07-12 included `ChatGPT-5.6:Luna`, `Terra`, `Sol`, `auto`, `embed`, and `gpt-4o:latest`. The web defaults were updated from stale `light/fast/large` tags to configurable Luna/Terra/Sol tiers.

The static Agent evaluation report is packaged separately:

```text
source:          evaluation-site/
public mapping:  https://agent-eval.k1412.top/
container port:  8080
health:          /health
deployment:      Unraid Compose Manager, app slug agent-eval
```

It uses no Ollama or runtime API. The source report remains usable even when the research application is
offline.

The 2026-07-27 deployment uses immutable image
`docker.k1412.top/wuyang/agent-eval:20260727T084425Z-cd1eacb` on NAS host port 12005. Container health,
NAS/VPS Tailscale reachability, HTTP-to-HTTPS redirect, HTTP/2, Let's Encrypt SAN, static-asset hashes,
public desktop/mobile rendering, and the homepage entry were all verified.

## Runtime Constraints

- Python standard library only; no package installation is required for the current web server and collection scripts.
- Ollama endpoint defaults to `http://192.168.1.10:11434`.
- One V100 32G serves all local models; AI calls are serialized.
- Do not send `num_ctx` or `keep_alive` from clients. Server-side context and residency must remain authoritative.
- `Sol` is the expensive tier and should require an explicit user action.
- The user mentioned an unlimited company GPT-5.5 data-agent API as a possible stronger offline worker, but no endpoint/authentication contract is committed. It is not currently integrated.
- The public site has no repository-managed authentication or TLS configuration; the domain mapping is external infrastructure.
- The committed systemd unit assumes the checkout path `%h/Code/agent`; edit the unit if a future checkout uses a different path.
- Full PDFs and extracted text used during the memory audit were temporary working files and are not committed. Their arXiv IDs and claim boundaries are preserved in `research/memory/evidence-ledger.md` so they can be fetched again.
- `web/cache/` contents, including arXiv responses, AI outputs, service environment and logs, are intentionally not committed.

## Validation

Offline validation:

```bash
python3 tools/check_project.py
python3 -m unittest tools.evaluation.test_score_agent_runs
git diff --check
```

Live validation:

```bash
web/manage.sh status
curl -fsS http://100.114.68.27:18080/api/health | jq
curl -fsS https://lab.k1412.top/api/health | jq
curl -fsS https://agent-eval.k1412.top/health
```

The health payload reports app/assets versions, paper count, available/default models, and missing default model tags.
