agent/docs/06-project-status.md

Project Status

last_reviewed: 2026-07-27 baseline_before_update: 8e4ff37 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.

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/	field findings and evidence ledger
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 field audit
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

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:

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 evaluation design follows a third line:

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.

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:

web/manage.sh start
web/manage.sh status

Expected addresses:

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:

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:

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

Live validation:

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.