Principle-first scientific idea discovery
Grounded in evidence. Structured for scrutiny. Exported for validation.
GitHub · PyPI · Documentation · Examples · Release QA
Ideas from principles. Validated by evidence.
Principia is a local-first Python framework that turns public literature and private research materials into traceable Idea Cards, prior-art comparisons, and validation-ready research packs.
Release scope: this README documents the maintained Principia v1.3.3 framework only.
Why Principia · Workflow · Installation · Examples · Trustworthiness · Research foundations · Architecture · Contact
Most LLM research assistants optimize for a convincing answer. Principia optimizes for an inspectable scientific object.
A serious research idea should make five things visible:
- Origin — which works and evidence records informed it;
- Principles — which mechanisms, constraints, and transferable abstractions it builds on;
- Construction — how those ingredients became the proposed method;
- Risk — which assumptions, failure modes, and prior-art overlaps may invalidate it;
- Testability — what experiment, baseline, metric, and falsification path should come next.
Principia therefore treats an idea as a typed research contract rather than a paragraph:
Idea Card
= selected evidence
+ reusable principles
+ explicit mechanism
+ novelty contrast
+ assumptions and risks
+ validation contract
The goal is not to generate more ideas. The goal is to generate ideas whose provenance, scientific logic, uncertainty, and next experiment are visible.
| Conventional workflow | Principia v1.3.3 |
|---|---|
| Ask an LLM to brainstorm from a prompt | Build an exact evidence packet before generation |
| Retrieve whole documents or opaque chunks | Extract typed ideas, principles, takeaways, comparators, evaluation contexts, and result facts |
| Accept a fluent one-shot proposal | Run a strict candidate–critique–evolution–selection process |
| Trust citations emitted by the model | Resolve every citation to a canonical (work_id, kind, record_id) |
| Hide provider failures behind generic output | Surface failures, allow one grounded repair, then fail closed |
| Leave the result in chat history | Export a portable, versioned research pack |
| Treat validation as future work | Make baselines, metrics, risks, and falsification part of the output |
Research goal
│
├── Public literature
│ arXiv · OpenAlex · Crossref · Semantic Scholar · Europe PMC
│
└── Private corpus (optional)
PDF · Office · Markdown · LaTeX · code · structured text
│
▼
Cross-domain retrieval and identity reconciliation
│
▼
Structured scientific feature extraction
ideas · principles · takeaways · baselines · benchmarks · result facts
│
▼
Canonical evidence packet
│
▼
SciDialect-Evo: 3 candidates → 2 evolved candidates → 1 final Idea Card
│
▼
Prior-idea comparison
│
▼
Deterministic validation plan + portable seven-file research pack
Principia is deliberately staged. Retrieval, extraction, evidence selection, generation, comparison, and export remain separately inspectable and resumable.
| Layer | Capability | Why it matters |
|---|---|---|
| Cross-domain retrieval | Query planning, arXiv/OpenAlex/Crossref/Semantic Scholar access, automatic Europe PMC routing for biomedical topics, bounded retries, identity reconciliation, BM25 or embedding reranking | A research idea should not be limited to one source, one naming convention, or one surface vocabulary |
| Private research context | Recursive local ingestion with portable local:// identities; core support for PDF, HTML/XML, Markdown, RST, LaTeX, text/code, JSON, YAML, CSV/TSV; optional DOCX/PPTX/XLSX support |
Public literature can be combined with private notes, unpublished drafts, internal reports, and project-specific evidence |
| Structured extraction | Typed prior ideas, principles, takeaways, comparators, evaluation contexts, and grounded result facts with provenance and content fingerprints | Principia reasons over scientific objects, not only document chunks |
| Canonical evidence | Exact record-level citations, source-text hydration, mixed public/private evidence checks, configurable global evidence budgets | The generator cannot silently invent the evidence it claims to use |
| Strict SciDialect-Evo | Three distinct proposals, explicit scoring, evolution of the strongest two, final selection, one evidence-grounded repair, fail-closed persistence | Idea generation becomes a controlled search process rather than a single completion |
| Prior-art comparison | Content-level shortlisting plus model-assisted comparison of mechanistic similarity, essential difference, potential advantage, and weakness | Novelty is treated as a contrastive claim, not a self-awarded label |
| Validation hand-off | Human-readable and JSON validation plans generated from the final Idea Card and canonical evidence without another LLM call | The research hypothesis and the experiment contract stay synchronized |
| Controllable jobs | Weighted progress, checkpoints, pause/resume/stop controls, notebook widgets, terminal display, event history | Long research runs remain observable and controllable while the worker process is active |
| Portable state | Shared project workspace, per-idea output folders, SQLite migrations, path-safe exports | Research memory can compound without duplicating the evidence pool |
Principia supports Python 3.10–3.13. The distribution name is principia-ai; the import package is principia.
python -m pip install principia-ai==1.3.3Add Office-document and notebook support when needed:
python -m pip install "principia-ai[local,notebook]==1.3.3"The examples below use SiliconFlow through an OpenAI-compatible API. Principia can also be configured with pc.LLMConfig(...) for other OpenAI-compatible endpoints.
export SILICONFLOW_API_KEY="your-key"
# Required to use OpenAlex under its current authentication policy.
export OPENALEX_API_KEY="your-openalex-key"Never commit credentials to source files, notebooks, .env files, or exported artifacts.
import os
import principia as pc
GOAL = (
"Develop an evidence-grounded method for improving long-horizon "
"reasoning efficiency in LLM agents under a fixed token budget."
)
ws = pc.Workspace.project(
"principia_project",
llm_config=pc.siliconflow_config(
os.environ["SILICONFLOW_API_KEY"],
max_calls=220,
),
)
job = ws.start(
GOAL,
pipeline_config=pc.PipelineConfig.research(),
)
result = job.result()
result.show()PipelineConfig.research() is the opinionated v1.3.3 research preset:
- exactly 50 public works requested;
- embedding reranking requested and reported explicitly;
- strict failure if the public target cannot be completed;
- an exact 15-record evidence packet: 5 ideas, 5 principles, and 5 takeaways;
- no more than 2 selected records from one work;
- strict
scidialect-evogeneration.
The preset is intentionally demanding. Every field can be overridden through PipelineConfig, RetrievalConfig, or the staged API.
import os
import principia as pc
ws = pc.Workspace.project(
"principia_project",
llm_config=pc.siliconflow_config(
os.environ["SILICONFLOW_API_KEY"],
max_calls=220,
),
allow_remote_private_content=True,
)
job = ws.start(
"Your research objective",
documents="private_sources",
pipeline_config=pc.PipelineConfig.research(),
)
result = job.result()
result.show()Private documents are supplemental: they never silently replace the requested public-literature target. Original files are not copied into the project. Absolute paths remain in hidden local state and are excluded from prompts and shareable exports.
Sending private text to a remote model requires explicit allow_remote_private_content=True. The provider receives document content and portable identifiers, not local absolute paths.
See Private corpus ingestion for parser limits, chunking, diagnostics, deduplication, custom parser registration, and cache cleanup.
The high-level workflow is concise, but the research process remains fully programmable:
works = ws.research.search(
GOAL,
target_count=50,
rerank_mode="embedding_rerank",
require_target=True,
show_progress=True,
)
features = ws.research.extract(
works,
model="auto",
show_progress=True,
)
evidence = pc.select_evidence(
features,
global_kind_limits={
"ideas": 5,
"principles": 5,
"takeaways": 5,
},
max_per_work=2,
require_exact=True,
user_note=GOAL,
)
idea = ws.ideas.generate(
evidence,
user_note=GOAL,
mode="scidialect-evo",
model="auto",
show_progress=True,
)
comparison = ws.ideas.compare(
idea,
features,
model="auto",
show_progress=True,
)This staged interface is useful when you need to inspect retrieval diagnostics, curate an evidence packet, replace a model, modify a generation budget, or stop before an expensive stage.
status = job.status()
print(status.status, status.stage, status.progress)
job.pause() # pause at the next safe provider boundary
job.resume()
job.stop() # schedule no further work
for event in job.events():
print(event)The same controls are available from the workspace or CLI while the original worker process remains active. Keep the persisted run ID so another notebook cell or terminal can address that worker:
run_id = job.run_id
ws.status(run_id)
ws.pause(run_id)
ws.resume(run_id)
ws.stop(run_id)Pause and stop are cooperative. An in-flight bounded provider request may finish and checkpoint, but no subsequent paid call begins after the relevant control boundary.
Run status, events, and completed stage checkpoints persist in SQLite. A Python
PipelineJob itself is thread-backed, however: reopening a workspace after its
worker process has exited does not recreate that thread or restart the remaining
pipeline automatically.
Workspace.project(...) separates reusable research state from idea-specific outputs:
principia_project/
workspace/
works.json
features.json
manifest.json
.principia/
outputs/
<idea_id>/
idea.md
idea.json
evidence.json
comparison.json
result.json
validation_plan.md
validation_plan.json
A generated Idea can include:
- title and one-sentence thesis;
- novelty claim and closest conceptual contrasts;
- mechanism design and methodological details;
- formulas and symbols when supported by evidence;
- method variants;
- validation protocol;
- baselines, metrics, risks, and assumptions;
- derived principles;
- exact source-evidence references;
- candidate, critique, evolution, selection, and repair metadata.
The validation plan is derived deterministically from the final Idea Card and selected evidence. It does not trigger another model call. Its Markdown and JSON forms preserve:
- the research goal and thesis;
- the proposed protocol;
- comparators and baselines;
- success and failure metrics;
- risks and assumptions;
- canonical evidence references;
- model, mode, schema version, and timestamps.
The result is designed to move cleanly into experiment repositories, coding agents, review workflows, or reproducibility bundles.
The release contains three compact, output-bearing acceptance showcases across AI, computer vision, and physics.
Each originating run used 50 public works + 5 local documents, produced 55 feature bundles, selected an exact 15-record evidence packet, ran live non-degraded scidialect-evo, compared the final idea with extracted prior ideas, and exported seven portable artifacts.
These examples demonstrate the framework's retrieval, grounding, generation, comparison, mathematics, privacy, and export contracts. Their Idea Cards are generated research hypotheses, not experimentally confirmed discoveries.
The excerpts below are rendered from the accepted live Idea Cards. Only Markdown wrapping and deterministic LaTeX notation normalization are applied; no scientific claim is rewritten.
The following acceptance metrics are generated directly from the verified showcase manifests:
| Task | Online | Local | Features | Evidence | Mode | Validation |
|---|---|---|---|---|---|---|
| Communication-efficient LLM multi-agent reasoning | 50 | 5 | 55 | 15 | scidialect-evo | passed |
| Uncertainty-aware sparse-view dynamic 3D reconstruction | 50 | 5 | 55 | 15 | scidialect-evo | passed |
| Broadband squeezed-state axion sensing | 50 | 5 | 55 | 15 | scidialect-evo | passed |
| Research task | Generated Idea Card | Review |
|---|---|---|
| Communication-efficient LLM multi-agent reasoning | Entropy-Constrained Discrete Codebook with Counterfactual Decoding and Diversity-Aware Calibration | Notebook · Showcase |
| Uncertainty-aware sparse-view dynamic 3D reconstruction | AnchorSplat-Dynamic: Sparse Anchor-Based Uncertainty for Uncalibrated Motion | Notebook · Showcase |
| Broadband squeezed-state axion sensing | Dynamic Heuristic Optimization of Squeezed-State Haloscopes with Continuum Noise Modeling | Notebook · Showcase |
Example 1 — Communication-efficient LLM multi-agent reasoning
Generated thesis
Imposing a per-task entropy floor on learned discrete messages prevents representational collapse while achieving token efficiency, provided that a counterfactual decoding protocol verifies causal interpretability and the entropy target is calibrated using real-time embedding diversity metrics.
Mechanism
Agents map internal states to indices in a shared codebook. The loss function includes a term penalizing codebook usage below a calculated entropy threshold (
Validation contract
Compare against standard CoT, uncompressed multi-agent baselines, and static codebook variants. Measure token reduction, accuracy, codebook entropy, success rate on counterfactual decoding tests, and embedding overlap. Specifically test scenarios where
The exported card used canonical evidence from five works and was compared against three extracted prior ideas.
Example 2 — Sparse-view dynamic 3D reconstruction
Generated thesis
By decoupling Gaussian primitives from the 2D pixel grid and anchoring them to a sparse set of 3D geometric proxies, we jointly optimize pose, motion, and heteroscedastic uncertainty for uncalibrated dynamic scenes, reducing redundancy in static regions while focusing capacity on moving objects.
Mechanism
A feed-forward encoder predicts sparse 3D anchors and associated Gaussian parameters (appearance, opacity, motion vectors, covariance). A differentiable renderer projects these into input views. A geometric prior module enforces epipolar and motion consistency constraints. An uncertainty head predicts per-Gaussian variance, gating gradient flow during test-time refinement to prevent overfitting to noise in uncalibrated views.
$$\mathcal{L}{\mathrm{total}} = \sum{i \in \mathcal{A}} (1 - \sigma_{u,i}) \cdot \mathcal{L}{\mathrm{render}}(i) + \lambda \mathcal{L}{\mathrm{geo}}(i)$$
Validation contract
Train on synthetic dynamic scenes (6–12 views). Evaluate on real-world uncalibrated sequences. Metrics: PSNR/SSIM, ECE, Risk-Coverage curves. Baselines: Dense pixel-aligned GS, Pose-dependent LRMs. Pass/Fail:
The exported card used five canonical records across four works and was compared against five extracted prior ideas.
Example 3 — Broadband squeezed-state axion sensing
Generated thesis
Integrating real-time heuristic search algorithms with Josephson Parametric Amplifiers allows for dynamic stabilization of squeezed states against environmental drift, while modeling noise propagation as a continuum through lossy transmission lines maximizes broadband scan speed and maintains false-positive controls via configuration-independent rejection.
Mechanism
A feedback loop where a heuristic search algorithm continuously adjusts JPA pump frequency and power based on real-time noise spectral density measurements derived from a continuum noise model. The system employs a dual-readout chain to enforce configuration-independent signal rejection, ensuring candidates are physical resonator responses rather than readout artifacts.
Validation contract
Validate squeezing advantage using reported metrics; verify candidate signals appear in both readout chains with predicted coherence width; test rejection of signals correlating with configuration changes; compare continuum noise model predictions against measured noise spectra.
The exported card used five canonical records across five works and was compared against three extracted prior ideas.
Principia does not treat provenance and privacy as presentation features. They are enforced at the data-model and persistence layers.
| Invariant | v1.3.3 behavior |
|---|---|
| Record-level evidence | Every citation must resolve to one canonical (work_id, kind, record_id) tuple. Quoted content is hydrated from the selected record rather than trusted from model output. |
| Evidence before generation | The generator receives an explicit EvidencePacket; configuration, traces, warnings, and usage metadata cannot become scientific evidence. |
| Fail-closed generation | Live output receives at most one evidence-grounded repair. If canonical references, grounding, required fields, or mathematical constraints remain invalid, the idea is not persisted. |
| No invisible live fallback | A failed live model call is surfaced. Deterministic mock output exists only as an explicitly labelled mock_fixture for tests and cannot satisfy release showcase gates. |
| Private by explicit consent | Local originals are not copied; absolute paths remain hidden; exported identifiers are portable; remote processing of private text requires explicit authorization. |
| Prompt-injection boundary | Retrieved and local documents are delimited as untrusted research data; embedded instructions are not treated as executable directives. |
| Observable retrieval | Diagnostics expose query plans, source health, retry outcomes, result counts, degraded-source warnings, ranking traces, and the rerank mode actually applied. |
| Strict mathematics | Retained formulas are normalized to canonical LaTeX, structurally validated, and included in strict KaTeX release checks. |
| Deterministic hand-off | ValidationPlan is generated from the accepted Idea Card and evidence registry without another probabilistic model call. |
Principia's grounding checks are recoverability checks, not a truth certificate. They ask whether canonical evidence references and the task-critical anchors needed to audit a proposal remain recoverable. Scientific validity still requires external evaluation.
The v1.3.3 release tree records a fail-closed acceptance process. Highlights include:
- the complete regression suite is rerun on Python 3.10, 3.12, and 3.13 against the final source and showcase artifacts before publication, with exact results recorded in the release QA report;
- CI coverage configured for Python 3.10–3.13;
- Ruff and mypy checks passed;
- wheel and source distribution build and
twine checkpassed; - clean-environment core and
[local]installation smokes passed; - strict source, export, notebook, LaTeX, and KaTeX audits passed;
- archive scans checked credentials, authorization headers, private paths, private sentinels, and stale artifacts;
- all three live research showcases passed their retrieval, evidence, generation, comparison, privacy, mathematics, and export gates.
See the complete Principia 1.3.3 Release QA report for exact commands, thresholds, warnings, and artifact checksums.
Principia is not merely a retrieval wrapper. It is a downstream research system built around a deeper question:
What representations should AI agents create, exchange, preserve, and evolve when the objective is scientific discovery rather than fluent conversation?
Two related research lines shape the framework.
Machine Dialectology studies how heterogeneous LLM agents can create, inherit, exchange, and route compact machine-oriented languages.
Its concrete ICML 2026 precursor is Communicative Language Symbolism Routing (CLSR):
CLSR treats a Language Symbolism Framework (LSF) as a reusable protocol rather than a shorter prose prompt. An LSF contains compact symbols, usage rules, validity constraints, and a message-passing contract. The system:
- samples seed exemplars;
- lets LLM agents invent candidate LSFs;
- generates LSF-conditioned responses;
- selects correct and token-efficient traces;
- evolves the protocol pool through propose → evaluate → select → mutate cycles;
- routes, ensembles, or composes LSFs at inference time.
The ICML paper reports a 3–6× reduction in latency-oriented generated-token completion relative to standard Chain-of-Thought while maintaining accuracy across the studied benchmarks.
The broader Machine Dialectology agenda generalizes this idea from one model family to heterogeneous machine societies. Its central insight is that machine-to-machine reasoning does not have to inherit the full rhetorical overhead of human prose. Agents can develop task-conditioned protocols whose intermediate states are denser, reusable, and routable.
Principia's connection: scientific ideation also has an intermediate-language problem. Mechanisms, trade-offs, assumptions, analogies, and falsification rules must be represented before they can be composed. Machine Dialectology provides the conceptual substrate for compact symbolic handles and reusable operators that help an agent move from literature evidence to structured, testable hypotheses.
SciDialect: Symbolic Compression as an Intrinsic Reward for Scientific Discovery Agents (Zhengqi Pei, Qingming Huang, and Shuhui Wang; technical manuscript, 2026) studies a complementary problem: compact symbolic representations are useful only when their scientific meaning survives compression.
Scientific agents often receive sparse external rewards. A hypothesis may not be rewarded until an experiment finishes; a coding approach may not be rewarded until hidden tests run; a research trajectory may not be rewarded until review. SciDialect introduces a denser representation-level signal: grounded symbolic compression.
A symbolic state earns credit only when it becomes shorter while preserving the task-critical anchors needed for independent reconstruction and audit.
An evidence anchor has the conceptual form:
anchor = (type, normalized value, source or location, role in the task)
Anchors may include variables, units, regimes, time windows, effect directions, equations, function signatures, dataset paths, output contracts, visible tests, prior-work links, or experimental controls.
A scientific dialect primitive is not an arbitrary abbreviation. It carries:
symbol
+ natural-language definition
+ argument schema
+ evidence slots
+ validity conditions
+ anti-definition of nearby invalid meanings
+ decoder template
Independent decoders must reconstruct the proposed meaning without access to the original verbose proposition. A skeptical reviewer checks missing anchors, added assumptions, semantic drift, invalid arguments, unsupported evidence, and dictionary bloat.
The intrinsic objective can be summarized as:
The terms reward compression, reconstruction fidelity, evidence grounding, decoder agreement, and optional development utility while charging dictionary growth. Reconstruction, grounding, agreement, dictionary-cost, leakage, and task-validity checks act as hard feasibility gates: a highly compressed but ungrounded state is never allowed to guide the solver.
This creates three important principles:
- No-free compression: a short private identifier has no scientific value when its definition cost and lost meaning are ignored;
- Amortized abstraction: a primitive is valuable only when reuse savings exceed definition, redundancy, and checking costs;
- Anchor-gated auditability: accepted states retain an explicit lower bound on recoverable task-critical anchors under the chosen grounding rule.
SciDialect does not certify that a claim is true. It certifies a stricter and more useful intermediate property: the compressed representation remains public enough to reconstruct, grounded enough to audit, and reusable enough to justify its symbolic cost.
Principia v1.3.3 implements a strict idea-evolution protocol inspired by this research:
3 mechanistically distinct candidate ideas
↓
score novelty · grounding · feasibility · discriminability
↓
evolve the strongest 2 with explicit critiques and recorded changes
↓
select 1 final evolved candidate with a rationale
↓
validate canonical evidence and scientific fields
↓
one grounded repair or fail closed
The broader SciDialect manuscript studies reward-conditioned symbolic scientific memory across several task adapters. Principia v1.3.3 implements the evidence-grounded idea-generation protocol relevant to this framework; it does not claim to reproduce every adapter or experiment in the broader manuscript.
| Research question | Research line | Principia consequence |
|---|---|---|
| What machine-oriented representations can agents invent, evolve, and route? | Machine Dialectology / CLSR | Use reusable symbolic protocols and task-conditioned operators rather than relying only on verbose prose |
| When should a compressed scientific representation be trusted? | SciDialect | Motivate Principia's canonical-evidence and audit checks; decoder-agreement and dictionary-cost objectives remain outside v1.3.3 |
| How can those representations become useful research outputs? | Principia | Convert an evidence packet into a traceable Idea Card, contrast it with prior ideas, and export a falsifiable validation contract |
Together, they move scientific-agent design from “generate a plausible proposal” toward “evolve an auditable representation that can survive comparison and experimentation.”
Principia v1.3.3 uses typed objects and explicit boundaries rather than a monolithic agent loop.
| Component | Responsibility |
|---|---|
principia_retrieval |
Source-aware query planning, provider clients, identity reconciliation, ranking, embeddings, retries, and diagnostics |
principia.research |
Public search, private ingestion, source acquisition, structured extraction, and checkpoint reuse |
principia.features |
Scientific feature schemas, evidence selection, canonical registries, validation, and source-text hydration |
principia.ideas |
Standard, calculus-compatible, and strict SciDialect-Evo generation; prior-idea comparison; fail-closed grounding checks |
principia.validation |
Deterministic validation-plan construction and Markdown/JSON rendering |
principia.pipeline |
Persisted end-to-end orchestration, weighted progress, pause/resume/stop, and result assembly |
principia.storage |
SQLite state, migrations, manifests, content fingerprints, portable artifacts, and private-cache controls |
principia.math |
LaTeX normalization, structural validation, and release-quality mathematical checks |
principia.cli |
Search, extraction, generation, run inspection, and run control from the terminal |
Public models, retrieval dataclasses, and job handles are importable directly from principia, including WorkItem, WorkFeatures, EvidencePacket, Idea, IdeaComparison, PipelineResult, ValidationPlan, RetrievalConfig, SciDialectConfig, SearchDiagnostics, and PipelineJob.
The package installs a principia command.
# Initialize and inspect a workspace
principia --workspace ./principia_project/workspace init
principia --workspace ./principia_project/workspace status
# Search public research metadata
principia --workspace ./principia_project/workspace \
search "evidence-efficient scientific agents" \
--target-count 20 \
--rerank-mode embedding_rerank \
--require-target
# Search, extract, and generate one strict SciDialect-Evo idea
principia --workspace ./principia_project/workspace \
generate "evidence-efficient scientific agents" \
--target-count 20 \
--rerank-mode embedding_rerank \
--require-target
# Inspect and control persisted runs
principia --workspace ./principia_project/workspace runs
principia --workspace ./principia_project/workspace status RUN_ID
principia --workspace ./principia_project/workspace pause RUN_ID
principia --workspace ./principia_project/workspace resume RUN_ID
principia --workspace ./principia_project/workspace stop RUN_IDUse --mock-llm only for deterministic smoke tests. Mock output is explicitly labelled and is not scientific evidence.
- API reference
- Projects, workspaces, and outputs
- Private corpus ingestion
- Background jobs and run control
- Trustworthy generation and mathematics
- Retrieval, diagnostics, and provider terms
- Publishing and release checks
- Examples
- Changelog
- Release QA
- Upstream integration and license scope
git clone https://github.com/pzqpzq/Principia.git
cd Principia/Principia-v1.3
python -m pip install -e ".[dev,local,notebook]"
python -m ruff check src tests scripts
python -m mypy src
python -m pytest -q
python -m build --no-isolation
python -m twine check dist/*Contributions are especially valuable in retrieval adapters, scientific schemas, evidence validation, domain-specific evaluation, private-document parsers, reproducibility tooling, and expert review of generated Idea Cards.
Principia is a research framework, not an oracle.
- Generated Idea Cards are hypotheses, not established results.
- Canonical evidence improves provenance; it does not guarantee that a source is correct.
- Prior-art comparison reduces obvious duplication risk; it does not replace a complete scholarly review or legal patent search.
- Validation plans structure the next experiment; they do not predict its outcome.
- Private documents should be sent to a remote model only under an appropriate data-governance policy.
- Scientific claims should be accepted only after independent empirical, theoretical, or expert validation.
The intended standard is simple: every persuasive claim should be paired with an inspectable source, an explicit assumption, or a test that could prove it wrong.
To cite Principia as software:
@software{principia2026,
title = {Principia: Principle-First Scientific Idea Discovery},
author = {{Principia Contributors}},
year = {2026},
version = {1.3.3},
url = {https://github.com/pzqpzq/Principia}
}To cite the CLSR / Machine Dialectology research foundation:
@inproceedings{pei2026lsf,
title = {When LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning},
author = {Pei, Zhengqi and Huang, Qingming and Wang, Shuhui},
booktitle = {Proceedings of the 43rd International Conference on Machine Learning},
year = {2026}
}Academic collaboration
In collaboration with the Institute of Computing Technology, Chinese Academy of Sciences.
Contact: peizhengqi22@mails.ucas.ac.cn
Business collaboration
In collaboration with Beijing Chipflow Technology Co., Ltd.
Contact: peizhengqi@chipflow.net
The principia-ai v1.3.3 framework is released under the MIT License.
The upstream multi-project repository contains separately scoped material. See UPSTREAM.md for the exact integration and license boundary.
Build ideas whose evidence, mechanism, and falsification path can be inspected.
If Principia is useful to your research, consider starring the repository and following the next releases in Principia, Machine Dialectology, and SciDialect.