agent/docs/07-exploration-history.md

Exploration History

last_reviewed: 2026-07-12

This is the decision history, including failed routes. Preserve it so future work does not rediscover the same lessons.

1. Repository and Content Streams

On 2026-07-08 the workspace was initialized as a long-lived Agent knowledge base, not a one-off report.

The first structure included stable knowledge, projects, experiments, references, and templates. It was then expanded into three first-class collection streams:

• jobs/: Beijing-focused Agent/LLM roles over a rolling six-month window;
• papers/: research evidence and reading queue;
• industry/: large-company reports, blogs, products, repositories, and benchmarks.

Decision retained: papers, jobs, and industry must not be mixed because they answer different questions. Their common organization layer is metadata/index/search, not a deep directory taxonomy.

Relevant commits: 47cea9d, 1b6cc1f, 36c2499.

2. First Collection and Expanded Corpus

The first sweep created 21 items and the source/schema workflow. The expanded arXiv sweep then recalled 1,506 candidates and promoted 970 high-relevance candidates, bringing the paper corpus to 974.

What worked:

• source registries and explicit source quality;
• keeping candidate and promoted manifests;
• generated frontmatter plus a deterministic index;
• preserving queued as a truthful unread state.

What did not work:

• treating the resulting count as understanding;
• assuming auto tags were a stable taxonomy;
• using non-paginated/latest-only query results to infer historical trends.

Relevant artifacts: collection-runs/, papers/corpus-summary-2026-07-08.md, data/arxiv-agent-*.json.

Relevant commits: 1cbc98e, 391b3a4.

3. Paper Browser to Knowledge Atlas

The first frontend exposed search, filtering, paper preview, abstract fetch, translation, summary, and deep analysis. It evolved into a knowledge Atlas with topic nodes, co-occurrence, timelines, routes, desktop/mobile layouts, and an ECharts graph.

Further iterations fixed:

• cache busting and stale deployment confusion;
• an oversized Atlas payload;
• desktop scrolling and reader focus;
• mobile map centering;
• interactive graph visuals;
• app version visibility.

This produced a usable corpus browser, but the user later rated its practical learning value at effectively 0/100. The reason was not mainly visual quality: exploration was stateless, and nothing helped the user retain, compress, challenge, or apply knowledge.

Decision retained: the Atlas may remain an orientation surface, but it must not be treated as the product or as evidence of learning.

Relevant commits: e783a7d through 15b5cec.

4. Search Agent and Per-Paper AI

Search was upgraded from keyword matching to a compact Agent loop:

interpret query -> expand Chinese/English terms -> retrieve locally
-> aggregate dates/topics/trends -> generate brief -> allow follow-up

The UI initially exposed every fixed pipeline step. That looked mechanical, so it was simplified into one active status row with a changing task summary. Paper reading also gained multi-turn chat.

What worked:

• AI-assisted query interpretation and bilingual expansion;
• progressive activity updates instead of a blocking spinner;
• contextual follow-up over a bounded result set;
• serialized model use and caching.

What remained insufficient:

• AI actions were still centered on individual papers or a single search;
• the system did not maintain claims, contradictions, the user's evolving model, or review state;
• a generated brief disappeared into another stateless browsing session.

Decision retained: future AI should operate at the claim/evidence/model level, not merely summarize or translate documents.

Relevant commits: 894c914, 4892e7a.

5. Ollama Integration Lessons

The application originally sent client-side context/residency parameters. The user identified that a num_ctx mismatch could restart a model and clear residency, and that keep_alive behavior could unload it.

The fix was to let the Ollama server own both settings. The application now only sends generation options such as temperature and output length, serializes calls, and caches results.

On 2026-07-12 the live model inventory no longer contained the old light/fast/large tags used by the web app. The defaults were changed to configurable tiers backed by the currently available Luna/Terra/Sol tags, and health now reports model mismatches.

Decision retained: always verify /api/tags; never infer deployed model aliases from old documentation.

Relevant commits: 47c301c, ac6e0e0, and the 2026-07-12 handoff update.

6. Learning-System Reframing

The user clarified the real objective with three levels:

1. complete memory of important knowledge;
2. compression into a coherent network/system;
3. abstraction that can explain why approaches exist and generate recommendations for new goals.

The desired system is a scaffold from raw papers to internalized judgment. The user expects Codex or another strong AI to be an active research partner, not merely an orchestrator for small local models.

This reframing invalidated “paper site” as the primary product definition.

7. Failed 12-Paper Learning Pilot

The first response to the reframing compiled 12 Agent Memory papers into a map, chapter, evidence matrix, active recall, and machine-readable module.

The user rejected it because:

• 12 papers were too few for a field-level trend;
• it felt like a lecture rather than joint research;
• it did not answer what 200+ papers were doing;
• it did not compare current failures with six months earlier;
• it did not establish whether architecture gains were attributable.

The experiment is explicitly failed in experiments/knowledge-compilation/2026-07-10-agent-memory-pilot.md. Its artifacts remain under learning/agent-memory/ only as historical material.

Decision retained: a polished educational package is not valuable until the underlying field audit and the user's own learning interaction are validated.

Relevant commit: 36c631e.

8. Agent Memory Field Audit

The failed pilot was replaced with a broader research audit:

• five paginated arXiv queries;
• 516 unique abstract-level candidates;
• complete local screening as triage;
• exact recent/prior six-month windows;
• 32 downloaded/text-located papers;
• 23 papers in an evidence ledger with explicit non-extrapolation boundaries.

The most useful findings were negative or diagnostic:

• retrieval quality often matters more than elaborate write structures for static QA;
• model and embedding choices can reverse architecture conclusions;
• update/forgetting and automatic writing remain unsolved;
• execution-state memory is the clearest task-specific capability direction;
• memory can interfere with other Agent components;
• cost and no-memory baselines are mandatory.

This audit is the current research baseline. Read research/memory/findings.md and research/memory/evidence-ledger.md rather than the old learning module.

Relevant commit: ab758e7.

9. Current Position

The repository now has a strong collection substrate, a functioning exploration prototype, and one evidence-led field audit. It does not yet have the stateful personal learning/internalization layer that motivated the project.

The next meaningful experiment is not another graph redesign or another bulk paper sweep. It is one end-to-end learning path that records a user's claims, evidence judgments, contradictions, applications, and later recall, using the memory audit as its first evidence base.