Document and harden project handoff
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wuyang
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# Next Work
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last_reviewed: 2026-07-12
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## P0: Build One Stateful Learning Path
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Use Agent Memory as the first vertical slice, but do not reuse the failed 45-minute lecture format.
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The unit of interaction should be a claim, not a paper. A minimal claim object needs:
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```text
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question
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claim
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supporting evidence
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counterevidence
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confidence
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scope / failure boundary
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prerequisites
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related claims
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user judgment
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last reviewed / next review
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```
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The first path should contain a small number of consequential claims from the existing audit, for example:
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- strong retrieval can dominate write complexity on static QA;
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- current-value maintenance is distinct from retrieval;
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- execution-state memory is different from historical recall;
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- automatic experience promotion requires evidence and rollback;
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- memory can have negative marginal value inside an Agent scaffold.
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The interaction should progressively reveal:
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```text
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problem -> current hypothesis -> evidence -> conflicting evidence
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-> user judgment -> application to a new design -> delayed recall
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```
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Persist the user's state. Merely rendering these sections is not enough.
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### P0 Data Work
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Define a versioned, inspectable local schema before adding a database. Suggested initial files:
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```text
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data/learning/claims.json
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data/learning/user-model.json
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data/learning/reviews.jsonl
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```
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Requirements:
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- every claim links back to evidence-ledger entries;
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- user notes and AI synthesis remain distinguishable;
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- claim updates preserve history;
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- confidence changes record a reason;
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- review records are append-only;
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- no generated summary can silently replace source evidence.
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### P0 AI Roles
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Use AI above the per-paper level:
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- evidence auditor: check whether a claim is actually supported;
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- contradiction finder: surface papers that challenge the current model;
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- synthesis maintainer: update the claim network when evidence changes;
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- research partner: help the user reason through an unfamiliar target;
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- review generator: create recall/application prompts from validated claims.
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Use Luna for cheap classification, Terra for retrieval briefs and bounded dialogue, and Sol only for explicit synthesis/critique. The user also has a company GPT-5.5 data-agent API with effectively unlimited tokens, but integration must wait until its endpoint, auth, rate behavior, and data policy are documented.
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### P0 Validation
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Do not judge success by page completion or time spent. Validate whether the user can:
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1. explain five claims without paper names;
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2. identify the evidence boundary of each claim;
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3. diagnose a new Agent memory design;
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4. predict when memory will hurt;
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5. recover the model after 1, 3, and 7 days;
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6. change a belief when presented with counterevidence.
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## P1: Connect Research to the Frontend
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The current Atlas/search/reader can remain, but it should feed the stateful path:
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- selecting a topic opens its unresolved questions and claims, not only a paper list;
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- selecting a paper shows which claims it supports or challenges;
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- search results can be promoted into an evidence review queue;
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- paper chat can create a candidate claim, never an automatically accepted fact;
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- the primary desktop focus should shift to the active research/learning task;
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- mobile should preserve the same conceptual flow with fewer simultaneous panes.
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Avoid another navigation-heavy redesign before the data and learning loop exist.
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## P1: Continue Collection Selectively
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Current gaps:
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- only 6 JD items, last checked 2026-07-08;
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- only 9 industry items;
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- 959 of 974 paper items are still queued;
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- paper source approval and automated collection policy need an explicit user decision;
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- the corpus is heavily biased toward May-July 2026 and broad arXiv query terms.
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Next collection work should be question-driven:
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- refresh Beijing DeepSeek/Tencent/ByteDance/Alibaba/Baidu roles and record changes;
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- add official company material that explains memory, evaluation, coding agents, or computer use;
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- deepen only papers that challenge or extend current claims;
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- do not add another thousand papers without a processing and evidence plan.
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After any item changes:
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```bash
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python3 tools/collection/build_index.py
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python3 tools/check_project.py
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```
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## P2: Research Refresh Protocol
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When refreshing the memory audit or starting another topic:
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1. define exact date windows and approved sources;
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2. save the broad candidate snapshot;
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3. separate deterministic filters from model triage;
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4. identify comparable benchmarks and controlled studies;
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5. read experiments, ablations, costs, and limitations in full text;
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6. update an evidence ledger before writing conclusions;
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7. preserve negative results and failed hypotheses;
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8. record the run under `experiments/` and update project status.
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Do not compare raw scores across papers unless model, embedding, prompt, visible information, and budget are controlled.
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## P2: Operations
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Still missing:
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- public-domain deployment documentation outside this repository;
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- authentication/authorization for a public or multi-user site;
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- automated backup for future personal learning state;
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- CI that runs `tools/check_project.py` on push;
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- tests for web API behavior and frontend flows.
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The repository now has an enabled user-level systemd service managed through `web/manage.sh`. It restarts on failure and survives user sessions because linger is enabled on the current host, but it is not a complete production deployment: proxy/TLS/auth configuration remains external.
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## Explicit Non-Goals
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- Do not make the graph more decorative as the next milestone.
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- Do not present 974 queued papers as knowledge already learned.
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- Do not revive the 12-paper lecture as the primary path.
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- Do not use per-paper summary/translation as the main AI value proposition.
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- Do not let AI write durable memory solely because a statement repeats in traces.
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- Do not hide model, embedding, context, or cost changes inside an architecture comparison.
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## First Continuation Task
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The best next task for another AI is:
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1. read the memory findings and evidence ledger;
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2. define the claim/user-state/review schemas;
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3. compile 5-8 audited claims into a stateful local dataset;
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4. implement one progressive learning/research view;
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5. run it with the user and record what changes in their understanding;
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6. keep or discard the interaction model based on that evidence.
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That is the shortest path from the current corpus browser toward the actual goal.
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