Pssa

PSSA: a plastic state-space architecture

PSSA is a small language model that is not a transformer. It reads text one
token at a time through a recurrent state-space layer, keeps a bank of episodic
memories it can look things up in, and rewrites part of its own weights while it
runs. It is written in Rust from scratch, with no PyTorch, no TensorFlow, and no
ML framework of any kind underneath it.

At matched parameters and on the same corpus, it learns faster than a
transformer and generates text about twelve times quicker on the same CPU.

Why Rust, and why that is not the point

Not for speed points, and not because the language makes the architecture
better. PSSA needed per-token weight updates, a memory bank written during the
forward pass, and a scalar reference path that every batched kernel could be
differentiated against. Expressing that inside an autograd framework meant
fighting the framework at every step, so the linear algebra is written
directly instead. That made the plastic parts straightforward and the
gradients checkable against a reference to around 3e-8. The architecture is
the claim here. The implementation language is a detail, and a Python port is
welcome.

How it differs from a transformer

A transformer scores every pair of tokens in the context, so its cost per step
grows with the square of the sequence length and the whole context is re-read at
every step. PSSA carries one fixed-size state along the sequence in a single
left-to-right pass, and looks things up in a memory bank instead of re-reading
the context, so cost grows linearly with length.

The model

Every token goes through one PSSA layer: a selective state-space recurrence,
a bounded read from an episodic memory bank in hyperbolic space, a learned gate
that decides how much of that read reaches the residual stream, and a SiLU MLP.
The defaults are d_m = 256 channels, d_s = 16 states per channel, and a
rank-16 adapter.

The recurrence

Write x for the layer-normalized token embedding. Three projections are read
off the token itself, which is what makes the recurrence selective rather than
fixed:

delta = softplus(W_delta x)      per-channel step size,  delta in R^d_m
B     = W_B x                    input map,              B in R^d_s
C     = W_C x                    output map,             C in R^d_s

The transition is diagonal, one rate per (channel, state) pair, kept negative by
construction so the recurrence cannot blow up:

A = -softplus(A_raw)             A in R^(d_m x d_s)

Discretizing that continuous system with step delta gives the per-token update.
h carries across tokens and across chunk boundaries during training:

Abar_ij = exp(delta_i * A_ij)
Bbar_ij = delta_i * B_j

h_ij <- Abar_ij * h_ij + Bbar_ij * x_i
y_i   = sum_j C_j * h_ij

A_raw is initialized so each channel's 16 rates sit on log-spaced timescales
tau from 1.5 to 200 tokens, in the spirit of the HiPPO initialization. A single
channel therefore starts out holding the last two tokens and the last two hundred
at the same time, and training moves those horizons rather than discovering them
from scratch.

This half of the layer is a selective diagonal SSM and claims no novelty; it is
the same family as S4 and Mamba, written out scalar-first so the backward pass
can be checked term by term.

The memory read

The part that is specific to PSSA is what happens to y. A query is formed from
both the current token and the current state, so retrieval is conditioned on
where the recurrence has got to and not only on the token in hand:

q  = W_qx x + W_qh y
qh = proj(q)                     diffeomorphic map into the Poincare ball, |qh| < 1

The read is bounded at four slots, weighted by a softmax over hyperbolic distance
at temperature tau_mem:

w = softmax(-d_H(qh, k_s) / tau_mem)   over the 4 nearest slots
m = sum_k w_k * v_k

Hyperbolic distance grows toward the boundary of the ball, so slots holding
general context and slots holding one specific episode stay separable without
widening the read. Four slots is a fixed cost per token regardless of how much
the bank holds.

Gate, adapter, MLP

The read does not join the stream unconditionally. A learned per-channel gate
decides how much of it lands, alongside a low-rank SiLU adapter that carries
targeted updates:

g     = sigmoid(W_gate x)
z     = s * y + g (elementwise) W_proj m + adapter(x)
u     = W_2 silu(W_1 z)
z_out = z + u

The write path

Writes are the reason the architecture is called plastic. A slot is inserted when
the incoming state is novel against what the bank already holds, each slot carries
a refractory counter that rate-limits how often it can be overwritten, and fast
plastic updates are folded back into the base transition matrix by a closed-form
ridge regression rather than living in the external store forever:

A_base <- A_base + (H^T H + lambda I)^-1 H^T dH

The refractory counter is what keeps a stream of contradictory updates from
erasing a slot that repeated evidence has already stabilized, and consolidation is
what stops the bank from being the only place long-range structure is stored.

What is and is not new here

The recurrence is standard selective-SSM machinery. The claims are the hyperbolic
bounded read conditioned on the recurrent state, the novelty and refractory rules
on writes, and the ridge consolidation step from fast weights into the transition
matrix. Everything is implemented against a scalar reference path that the batched
and parallel implementations are differentiated against on every commit, currently
agreeing to a maximum gradient error around 3e-8 (cargo run --release --example twin_check).

The result

Two models, same corpus, same tokenizer, same optimizer schedule, same seed,
same number of parameters. One is PSSA, one is a standard transformer. Over
12.7M tokens of cleaned WikiText-103:

PSSA finished at 3.98 training cross-entropy, the transformer at 4.43.
That is a gap of 0.45 nats, perplexity 53.7 against 83.7. The transformer
spent its entire 12.7M-token budget to reach a loss PSSA had already passed
around 2M tokens in.

The two curves never cross, and they never touch. Here is the PSSA run on its
own, every logged update across the chain:

29,243 logged updates, 7.63 down to 3.98, with a 41-point moving average drawn
over the raw ticks.

It holds on text neither model has seen

Training loss only says a model fit the stream it was fed. So both checkpoints
were scored on a 198,939-token slice cut from a part of the corpus neither run
ever touched:

Every checkpoint of both runs, 64 PSSA links and 43 transformer links, scored on
a bounded 9,934-token window of that unseen slice. The curves never cross: PSSA
is ahead from the first link and finishes 0.51 nats lower. The table below is the
final checkpoint of each run on the full slice.

Held-out slice, 198,939 unseen tokens	PSSA	Transformer
Cross-entropy	3.997	4.429
Perplexity	54.4	83.8
Next-token accuracy	24.1%	18.0%

The held-out gap, 0.43 nats, is essentially the training gap. PSSA is not
memorizing harder, it is generalizing better.

And it is much faster to run

Fixed work on the same 2 vCPU machine, 199,059 tokens at 512 tokens per update:
1,716 tokens per second against 415, so 4.13x. Both models were timed on CPU.

Both architectures put their optimum at the same learning rate, 0.003, so
neither run is winning on a tuning advantage. The sweep is a short probe on a
120,000-token slice, a settings check rather than a final number.

Generating 200 tokens on the same CPU, same prompt, same sampler:

	PSSA	Transformer
200 tokens	226 ms	2,735 ms
Relative	12x faster	baseline

A recurrent model carries a fixed-size state, so the cost of each new token does
not grow with the length of what came before. A transformer re-reads its whole
context every step.

What is actually different about it

• A recurrent state-space core. Learned continuous state matrices carry
  information forward in a fixed-size state, instead of attention over the full
  context window.
• An episodic memory bank. 512 slots with hyperbolic (Poincare-style)
  retrieval and bounded top-4 search, written to and read from during the run.
• Plastic weights. Fast updates reinforce what works, novelty drives growth,
  and a refractory gate rate-limits overwrites so repeated contradictory input
  does less damage.
• Closed-form consolidation. A ridge-regression step folds the fast plastic
  updates back into the base transition matrix, the way sleep consolidates a
  day's learning.
• No framework. Hand-written linear algebra in Rust, with a CUDA path for
  training and a scalar CPU reference that every gradient is checked against
  (max gradient difference 2.98e-8).

What this is not

Being straight about the scale, because the numbers above are easy to
over-read:

• These are 1.5M-parameter models on 12.7M tokens. That is a research
  prototype, not a competitor to anything you have heard of.
• Text quality at this scale is poor for both models. PSSA emits "a barget of
  the Prian Academy", the transformer "a material circulation of the United
  States". The comparison is about learning efficiency, not fluency.
• The speed comparison is CPU-to-CPU, which is fair. The training throughput
  numbers further down are not hardware-matched and should not be read as an
  architecture result.
• Two experiments are still unmeasured: retention of earlier skills after a
  corpus switch, and whether ablating the memory bank changes the loss.

Try it

git clone https://github.com/Sparticle62ops/pssa.git
cd pssa
cargo build --release
./target/release/oxide_ai_pssa

Running it with no arguments gives you a home screen listing every command plus
any checkpoint and corpus it finds in the working directory.

Where the project needs help

Compute

The whole result above was trained on a free hosted notebook with a single
entry-level GPU, in 200,000-token links, because a session gets cut after a few
hours. Every interesting question left, whether the gap holds at 10x or 100x
these parameters, whether the memory bank matters at scale, how it does against
a modern recurrent baseline, needs one thing: a GPU with real VRAM and
allocations measured in days instead of hours. Anything meaningfully above the
entry-level card this ran on changes what can be asked.

If you have compute to grant, or you work somewhere that does, that is the
single highest-leverage thing anyone can offer this project.

Sponsorship

Sponsorship funds compute and nothing else. In return you get named here and in
the write-up of any result your hardware made possible. Get in touch before
sending anything so the details can be agreed.

Donate

Solana: 4XPZ9uAa2BMoth6msoHRxTWL4mUrMfq3LGrxbAGja96h

---

Setup and codebase

Everything below is for running, training, and working on the project.

Requirements

• Rust toolchain with Edition 2024 support, including Cargo.
• Network access only when using an HTTP/HTTPS dataset or a Hugging Face dataset.
• Enough memory and disk for larger corpora and serialized models.
• Optional: a CUDA device for the GPU training path. The CPU path is the
  reference and always available.

Direct runtime dependencies are ureq for
dataset downloads and tokenizers for
byte-level BPE. A GPU is optional. Built with --features cuda the dense
matrix work dispatches through cuBLAS with a device-resident weight cache; a
WebGPU adapter is used for the same stages when CUDA is unavailable, and
software adapters are refused because they are slower than the CPU path.
Everything falls back to the CPU implementation with no feature flags.

How the comparison was run

How the two runs were matched

Both chains ran 64 links of 200,000 encoded tokens, each link resuming from the
previous checkpoint, so the learning-rate schedule and optimizer state continue
across the whole run instead of restarting per link.

• Identical corpus: one clean-wikitext pass over WikiText-103, reused byte for byte.
• Identical token IDs: the baseline pins --tokenizer-from to the PSSA chain's
  own checkpoint, so neither model sees a different vocabulary.
• Identical optimization: 30,000-update cosine horizon, no warm-up restart, 512
  supervised target tokens per update, seed 42.
• PSSA: latent 256, recurrent state 16, 512 memory slots, key width 32, vocab 2,048.
• Baseline: 1,541,120 parameters, 1 layer, width 256, 4 heads, FFN 448, vocab 2,048.

Matching the optimizer schedule cuts one way and not the other: neither model
received tuning the other did not, but a schedule that suits PSSA is not
guaranteed to be the transformer's best, so part of the gap could be an
undertrained baseline rather than the architecture. A per-model learning-rate
sweep is running now, both models swept over the same grid on the same token
budget, and the best-against-best numbers will be posted here when it
finishes, whichever way they come out.

Per-token learning curve

End-of-link training cross-entropy:

Link	Tokens seen	PSSA	Transformer
ck01	200,000	5.733	6.461
ck05	1,000,000	4.617	5.467
ck10	2,000,000	4.447	5.082
ck15	3,000,000	4.292	4.858
ck20	4,000,000	4.185	4.704
ck25	5,000,000	4.221	4.704
ck30	6,000,000	4.070	4.561
ck35	7,000,000	4.039	4.523
ck37	7,400,000	3.960	4.465
ck44	8,800,000	4.004	4.480
ck48	9,600,000	3.937	4.415
ck52	10,400,000	3.846	4.344
ck56	11,200,000	3.887	4.375
ck60	12,000,000	3.972	4.418
ck64	12,800,000	3.982	4.428

The baseline's first session was cut at link 43 by the notebook session limit
and its loss CSV did not survive, so links 1 to 43 are read back from that
session's own run log instead. The chain resumed from ck43 in a second session
and finished all 64 links, and both curves above now cover the full run.

Throughput is not hardware-matched

The headline training rates come from different machines and say nothing on
their own: PSSA trained on a Kaggle T4 at roughly 900 tokens/second, while the
baseline is CPU-only because train-transformer has no GPU path, and held 212
tokens/second there.
For a comparison that means something, both models were trained on the same
CPU-only box, a 2-vCPU container with no GPU, over the same 199,059-token
slice of the cleaned corpus with seed 42 and identical update counts. PSSA
held 1,716 tokens/second against the baseline's 415, so 4.1x on matched
hardware and matched work. An earlier measurement on Kaggle's CPU, before the
scan parallelization, put the same pair at 375 against 212.
The loss comparison above is unaffected either way, since it is matched on
tokens and updates rather than on time.

What these numbers are, and are not

The losses are end-of-link training cross-entropy on the stream being fit, not
held-out evaluation. For a held-out comparison on an unseen slice, use the
compare command described in docs/COMPARISON.md.
Generation quality at this scale is poor for both models: PSSA emits "a barget
of the Prian Academy", the baseline "a material circulation of the United
States".

Two experiments are not yet measured: retention of earlier skills after a
corpus switch, and whether ablating the 512 memory slots changes loss.

Reproducing

bash kaggle/kaggle_continue.sh              # the PSSA chain
bash kaggle/kaggle_transformer_baseline.sh  # the parameter-matched baseline

Both read TOTAL, WINDOW and FRESH from the environment and write
--loss-csv, so the curve survives a cut session.

CLI Reference

General form:

oxide_ai_pssa <COMMAND> [OPTIONS]

Commands:

Command	Purpose
train [source]	Fit a checkpoint on a text corpus and write a .pssa file.
generate <prompt>	Continue a prompt with a trained checkpoint.
chat [source] or repl [source]	Interactive prompt loop against a checkpoint.
evaluate [source]	Cross entropy, perplexity and accuracy as JSON.
status	Checkpoints and corpora in the working directory. Takes no options.
download <repo>	Pull a Hugging Face dataset to a local file.
clean-wikitext INPUT -o OUTPUT	Stream-clean a raw WikiText file into a new UTF-8 corpus.
benchmark	End-to-end smoke test on the built-in corpus.
gpu-probe	Check whether a WebGPU compute device is usable.
help	Print command and option help.

Options:

Option	Default	Applies to	Description
-d, --data <source>	data/downloaded.txt when present, otherwise science	train, chat, evaluate	Dataset source, or a comma-separated list.
-m, --model <path>	data/model.pssa	chat, generate, evaluate	Checkpoint to load.
-o, --out <path>	Command-specific; required for clean-wikitext	train, download, clean-wikitext	Output checkpoint or dataset path. Cleaning requires a new file.
-p, --prompt <text>	empty	generate	Prompt text. Required for generation.
-e, --epochs <n>	4	train	Training epochs.
-t, --temp, --temperature <float>	0.70	chat, generate	Sampling temperature.
--max-new-tokens <n>	64 (maximum 100,000)	generate	Generation length cap.
--latent <n>	256	train	Latent dimension.
--state <n>	16	train	Recurrent state dimension.
--key <n>	32	train	Memory-key dimension.
--memory <n>	512	train	Memory bank capacity.
--chunk <n>	64	train	Sequence chunk length.
--lr <float>	1e-3	train	Base learning rate.
--accumulate <n>	8	train	Chunks per optimizer update.
--warmup-steps <n>	0	train	Linear warm-up before cosine decay.
--seed <n>	42	train	Initialization seed.
--tokenizer <bpe|word>	bpe	train	Tokenizer family.
--vocab-size <n>	2048	train	BPE vocabulary maximum.
--max-tokens <n>	unset	train	Global cap across input documents, not per document.
--skip-tokens <n>	0	train	Skip this many tokens before training starts.
--resume <path>	unset	train	Continue from an existing checkpoint.

Positional arguments and long/short options can be mixed:

cargo run --release -- train data/downloaded.txt -e 2 -o data/experiment.pssa
cargo run --release -- train --data data/downloaded.txt --epochs 2 --out data/experiment.pssa

Chat commands

Inside the REPL:

• /exit or quit exits the process.
• /info prints the loaded model path, memory slot count, and adapter count.
• /temp <value> reports a temperature value but does not apply it to later turns. Pass --temp when launching chat instead.

Training over a long corpus

--skip-tokens, --max-tokens and --resume together let a long corpus be trained as a chain of short runs, so a single run never has to survive a session limit. If a window crosses EOF, selection wraps to the beginning of the corpus. Each link trains its own window and hands its optimizer state to the next:

cargo run --release -- train data/downloaded.txt -e 1 \
  --skip-tokens 0      --max-tokens 200000 -o chain/ck01.pssa
cargo run --release -- train data/downloaded.txt -e 1 \
  --skip-tokens 200000 --max-tokens 200000 --resume chain/ck01.pssa -o chain/ck02.pssa

kaggle/kaggle_continue.sh drives this pattern end to end: it sets a window size and a link count, walks the corpus offset by offset, and resumes each link from the previous checkpoint. status then reports every checkpoint in the chain with its shape and optimizer step count.

Dataset Sources

DatasetManager accepts one or more comma-separated sources:

cargo run --release -- train science                       # built-in reference corpus
cargo run --release -- train data/downloaded.txt           # local text file
cargo run --release -- train data/                         # every readable file in a directory
cargo run --release -- train https://example.org/corpus.txt
cargo run --release -- train hf:owner/dataset              # Hugging Face repository
cargo run --release -- train science,data/downloaded.txt   # multiple sources

Local files and directories are read directly; HTTP(S) URLs and explicit hf:owner/dataset
sources are downloaded. Structured responses are reduced using common fields such as
text, content, article, story, instruction, output, sentence, and summary;
structured responses without a supported text field are rejected.

Byte-level BPE keeps exact UTF-8 case, whitespace, punctuation, and line endings, and has a complete 256-byte fallback alphabet, so valid UTF-8 never collapses to <unk>. The previous lowercase word splitter, including its 10,000-word cap and <unk> behavior, is available only with --tokenizer word.

Download a Hugging Face dataset into a local text file:

cargo run --release -- download wikimedia/wikipedia --out data/downloaded.txt

Network downloads are not validated or curated by Oxide AI. Review licensing, privacy, and content before training on an external corpus.

Cleaning WikiText raw corpora

Clean extracted wikitext-103-raw text before a fresh training run:

./target/release/oxide_ai_pssa clean-wikitext wiki.train.raw --out data/wikitext-clean.txt
./target/release/oxide_ai_pssa train data/wikitext-clean.txt -o data/model.pssa
# Also available: oxide_ai_pssa help clean-wikitext

The same command can be used in Kaggle after extracting text from Parquet; it
accepts a local UTF-8 text file, not Parquet itself. -o and --out are aliases.
The output path is required and must not already exist (including the input
path or a link to it). This protects the original corpus; choose a new output
name for another run. Read, UTF-8, and write failures exit nonzero through the
normal CLI error path, with partial output removed when possible.

The pass:

• Joins @-@, @.@, and @,@ to adjacent text: guest @-@ starring →
  guest-starring, 52 @.@ 9 → 52.9, 500 @,@ 000 → 500,000.
• Drops balanced heading lines such as = Title = and = = Section = =.
• Removes <unk> and collapses remaining inline whitespace to single spaces.
• Removes spaces before ., ,, ) and after (; trims each line.
• Retains at most one consecutive blank line, including at the start/end.
  Removing a heading does not introduce a blank line.
• Writes LF line endings, including a newline on the last retained line.

oxide_ai_pssa::dataset::clean_wikitext(reader, writer) is the reusable library
API (BufRead / Write, returning std::io::Result<()>). The CLI uses buffered
file I/O, and the cleaner retains only its input/output line buffers: memory is
proportional to the longest line, not the corpus size. Library callers using a
buffered writer must flush it themselves; the CLI explicitly checks the flush.
No new dependencies are required.

Cleaning is opt-in: existing loaders, tokenizers, training commands, and
kaggle/kaggle_continue.sh are unchanged. Do not switch an in-flight resume
chain to a cleaned corpus: cleaning changes token IDs/counts and the meaning
of --skip-tokens offsets. Prepare and consistently reuse one cleaned corpus
for a new chain instead.

Training Pipeline

The train command performs two phases:

1. Continuous recurrent ingestion: token transitions are processed through the PSSA layer. The model updates state, memory, adapters, and routing behavior with a cosine learning-rate schedule.
2. Adapter consolidation: after each epoch, the plastic adapter's fast coefficients are folded into its consolidated coefficients with the configured EMA rate.

Defaults are latent 256, recurrent state 16, memory-key 32, memory capacity 512, chunk length 64, learning rate 1e-3, 8 chunks per update, and seed 42. The resulting binary holds weights, configuration, memory, adapters, and optimizer state. It is not an interchange format for other ML frameworks and should be loaded through PSSALayer::import_from_pssa_bytes.

Checkpoint compatibility

New saves use V7: the full V6 training/resume payload plus a bounded, length-prefixed standard tokenizer JSON. A V7 BPE checkpoint is self-contained and restores its exact ordered vocabulary without access to the training or evaluation corpus. generate and chat reject --data for V7 BPE because retraining a tokenizer on external data would not validate provenance. V7 word checkpoints and V6 checkpoints retain the legacy optional --data exact-vocabulary comparison. Checked V5 artifacts remain inference-only and require --data because they never contained tokenizer provenance.

Inference

Generation is autoregressive and uses temperature 0.70, a top-24 candidate limit followed by top-p 0.85 filtering, a 1.25 repetition penalty over a recent 64-token window, immediate self-transition suppression, <unk> suppression, and a default cap of 64 new tokens, ending early after two generated periods.

V7 BPE inference restores the exact embedded tokenizer and never rebuilds it from a selected dataset. Evaluation supplies its data only as held-out text to the restored tokenizer.

Benchmark

cargo run --release -- benchmark

The suite exercises synthetic streams for contradictory facts, MQAR-style distractors, burst repetition, model serialization, and short generation prompts. It prints milestone results, is not wired into Cargo's test harness, and is not a quality evaluation on general language tasks.

Project Layout

Path	Responsibility
src/main.rs	Binary entry point; forwards process arguments to the CLI.
src/cli.rs	Argument parsing, home screen, training, chat, generation, evaluation, status, download, and benchmark orchestration.
src/ui.rs	Terminal presentation: logo, panels, spinners, progress bars, ANSI-aware width handling.
src/dataset.rs	Tokenization, vocabulary construction, built-in corpora, local and remote loading, streaming WikiText cleaning.
src/pssa.rs	PSSA layer, forward pass, plastic learning, consolidation, and .pssa serialization.
src/checkpoint.rs	Checkpoint format versions, resume payloads, and import/export validation.
src/inference.rs	Autoregressive sampling and generation constraints.
src/backend.rs	GEMM dispatch, CPU reference kernels, and the WebGPU device probe.
src/memory.rs	Fixed-capacity hyperbolic memory bank and retrieval/update logic.
src/adapter.rs	Low-rank modular adapter projections and updates.
src/defense.rs	Refractory rate-limiter primitives for stable updates and overwrite defense.
src/linalg.rs	Small allocation-conscious vector, matrix, math, and deterministic RNG utilities.
src/diagnostics.rs	CLI banner formatting.
kaggle/	Chained-training driver for long corpora on a hosted notebook.
data/downloaded.txt	Checked-in corpus used as the default when present.
data/model.pssa	Checked-in serialized model artifact.

Development

cargo fmt --all -- --check
cargo clippy --release --all-targets
cargo test --release

Integration tests live in tests/: allocations.rs, bpe_repair.rs, checkpoint_repair.rs, core_repair.rs, linalg.rs, and runtime_repair.rs, with shared artifacts under tests/fixtures/. They cover tokenizer round trips, checkpoint import/export across versions, linear-algebra kernels, allocation behavior, and CLI runtime output. Clippy is clean of errors; a number of style warnings in the numeric kernels are left in place deliberately, since rewriting indexed loops there would churn code the gradient tests pin down.

Limitations

• CPU-oriented prototype with hand-written linear algebra. gpu-probe verifies a WebGPU device and a GEMM against the CPU reference, but training and inference still run the layer math on the CPU.
• The CLI parser is intentionally minimal: no shell-style quoting, and little validation beyond numeric parsing.
• A missing or unreadable dataset silently falls back to the built-in science corpus in several loading paths.
• Model and tokenizer vocabularies must remain compatible; a size warning does not repair a mismatch.
• Model shape cannot change across a resume chain: latent, state, key, memory and vocabulary must match the checkpoint being resumed.
• Downloaded content can be large and may contain JSON, malformed text, or data unsuitable for training.
• The REPL temperature command acknowledges a value without changing the active configuration.
• Benchmark output is milestone-oriented and does not measure perplexity, factuality, latency, or safety.
• Serialized .pssa files are project-specific binary artifacts without version migration tooling.

data/downloaded.txt

Anarchism is a political philosophy and movement that is skeptical of all justifications for authority and seeks to abolish the institutions it claims maintain unnecessary coercion and hierarchy, typically including nation-states, and capitalism. Anarchism advocates for the replacement of the state with stateless societies and voluntary free associations. As a historically left-wing movement, this reading of anarchism is placed on the farthest left of the political spectrum, usually described as the libertarian wing of the socialist movement (libertarian socialism).  Humans have lived in societies without formal hierarchies long before the establishment of states, realms, or empires. With the rise of organised hierarchical bodies, scepticism toward authority also rose. Although traces of anarchist ideas are found all throughout history, modern anarchism emerged from the Enlightenment. During the latter half of the 19th and the first decades of the 20th century, the anarchist movement flourished in most parts of the world and had a significant role in workers' struggles for emancipation. Various anarchist schools of thought formed during this period. Anarchists have taken part in several revolutions, most notably in the Paris Commune, the Russian Civil War and the Spanish Civil War, whose end marked the end of the classical era of anarchism. In the last decades of the 20th and into the 21st century, the anarchist movement has been resurgent once more, growing in popularity and influence within anti-capitalist, anti-war and anti-globalisation movements.  Anarchists employ diverse approaches, which may be generally divided into revolutionary and evolutionary strategies; there is significant overlap between the two. Evolutionary methods try to simulate what an anarchist society might be like, but revolutionary tactics, which have historically taken a violent turn, aim to overthrow authority and the state. Many facets of human civilization have been influenced by anarchist theory, critique, and praxis.  Etymology, terminology, and definition   The etymological origin of anarchism is from the Ancient Greek anarkhia, meaning "without a ruler", composed of the prefix an- ("without") and the word arkhos ("leader" or "ruler"). The suffix -ism denotes the ideological current that favours anarchy. Anarchism appears in English from 1642 as anarchisme and anarchy from 1539; early English usages emphasised a sense of disorder. Various factions within the French Revolution labelled their opponents as anarchists, although few such accused shared many views with later anarchists. Many revolutionaries of the 19th century such as William Godwin (1756-1836) and Wilhelm Weitling (1808-1871) would contribute to the anarchist doctrines of the next generation but did not use anarchist or anarchism in describing themselves or their beliefs.  The first political philosopher to call himself an anarchist () was Pierre-Joseph Proudhon (1809-1865), marking the formal birth of anarchism in the mid-19th century. Since the 1890s and beginning in France, libertarianism has often been used as a synonym for anarchism and its use as a synonym is still common outside the United States. Some usages of libertarianism refer to individualistic free-market philosophy only, and free-market anarchism in particular is termed libertarian anarchism.  While the term libertarian has been largely synonymous with anarchism, its meaning has more recently been diluted by wider adoption from ideologically disparate groups, including both the New Left and libertarian Marxists, who do not associate themselves with authoritarian socialists or a vanguard party, and extreme cultural liberals, who are primarily concerned with civil liberties. Additionally, some anarchists use libertarian socialist to avoid anarchism's negative connotations and emphasise its connections with socialism. Anarchism is broadly used to describe the anti-authoritarian wing of the socialist movement. Anarchism is contrasted to socialist forms which are state-oriented or from above. Scholars of anarchism generally highlight anarchism's socialist credentials and criticise attempts at creating dichotomies between the two. Some scholars describe anarchism as having many influences from liberalism, and being both liberal and socialist but more so. Many scholars reject anarcho-capitalism as a misunderstanding of anarchist principles.  While opposition to the state is central to anarchist thought, defining anarchism is not an easy task for scholars, as there is a lot of discussion among scholars and anarchists on the matter, and various currents perceive anarchism slightly differently. Major definitional elements include the will for a non-coercive society, the rejection of the state apparatus, the belief that human nature allows humans to exist in or progress toward such a non-coercive society, and a suggestion on how to act to pursue the ideal of anarchy.  History  Pre-modern era   Before the creation of towns and cities, established authority did not exist. It was after the institution of authority that anarchistic ideas were espoused as a reaction. The most notable precursors to anarchism in the ancient world were in China and Greece. In China, philosophical anarchism (the discussion on the legitimacy of the state) was delineated by Taoist philosophers Zhuang Zhou and Laozi. Alongside Stoicism, Taoism has been said to have had "significant anticipations" of anarchism.  Anarchic attitudes were also articulated by tragedians and philosophers in Greece. Aeschylus and Sophocles used the myth of Antigone to illustrate the conflict between laws imposed by the state and personal autonomy. Socrates questioned Athenian authorities constantly and insisted on the right of individual freedom of conscience. Cynics dismissed human law (nomos) and associated authorities while trying to live according to nature (physis). Stoics were supportive of a society based on unofficial and friendly relations among its citizens without the presence of a state.  In medieval Europe, there was no anarchistic activity except some ascetic religious movements. These, and other Muslim movements, later gave birth to religious anarchism. In the Sasanian Empire, Mazdak called for an egalitarian society and the abolition of monarchy, only to be soon executed by Emperor Kavad I.  In Basra, religious sects preached against the state. In Europe, various sects developed anti-state and libertarian tendencies. Renewed interest in antiquity during the Renaissance and in private judgment during the Reformation restored elements of anti-authoritarian secularism, particularly in France. Enlightenment challenges to intellectual authority (secular and religious) and the revolutions of the 1790s and 1848 all spurred the ideological development of what became the era of classical anarchism.  Modern era  During the French Revolution, partisan groups such as the Enragés and the  saw a turning point in the fermentation of anti-state and federalist sentiments. The first anarchist currents developed throughout the 18th century as William Godwin espoused philosophical anarchism in England, morally delegitimising the state, Max Stirner's thinking paved the way to individualism and Pierre-Joseph Proudhon's theory of mutualism found fertile soil in France. By the late 1870s, various anarchist schools of thought had become well-defined and a wave of then unprecedented globalisation occurred from 1880 to 1914. This era of classical anarchism lasted until the end of the Spanish Civil War and is considered the golden age of anarchism.  Drawing from mutualism, Mikhail Bakunin founded collectivist anarchism and entered the International Workingmen's Association, a class worker union later known as the First International that formed in 1864 to unite diverse revolutionary currents. The International became a significant political force, with Karl Marx being a leading figure and a member of its General Council. Bakunin's faction (the Jura Federation) and Proudhon's followers (the mutualists) opposed state socialism, advocating political abstentionism and small property holdings. After bitter disputes, the Bakuninists were expelled from the International by the Marxists at the 1872 Hague Congress. Anarchists were treated similarly in the Second International, being ultimately expelled in 1896. Bakunin famously predicted that if revolutionaries gained power by Marx's terms, they would end up the new tyrants of workers. In response to their expulsion from the First International, anarchists formed the St. Imier International. Under the influence of Peter Kropotkin, a Russian philosopher and scientist, anarcho-communism overlapped with collectivism. Anarcho-communists, who drew inspiration from the 1871 Paris Commune, advocated for free federation and for the distribution of goods according to one's needs.  By the turn of the 20th century, anarchism had spread all over the world. It was a notable feature of the international syndicalist movement. In China, small groups of students imported the humanistic pro-science version of anarcho-communism. Tokyo was a hotspot for rebellious youth from East Asian countries, who moved to the Japanese capital to study. In Latin America, Argentina was a stronghold for anarcho-syndicalism, where it became the most prominent left-wing ideology. During this time, a minority of anarchists adopted tactics of revolutionary political violence, known as propaganda of the deed. The dismemberment of the French socialist movement into many groups and the execution and exile of many Communards to penal colonies following the suppression of the Paris Commune favoured individualist political expression and acts. Even though many anarchists distanced themselves from these terrorist acts, infamy came upon the movement and attempts were made to prevent anarchists immigrating to the US, including the Immigration Act of 1903, also called the Anarchist Exclusion Act. Illegalism was another strategy which some anarchists adopted during this period.  Despite concerns, anarchists enthusiastically participated in the Russian Revolution in opposition to the White movement, especially in the Makhnovshchina; however, they met harsh suppression after the Bolshevik government had stabilised, including during the Kronstadt rebellion. Several anarchists from Petrograd and Moscow fled to Ukraine, before the Bolsheviks crushed the anarchist movement there too. With the anarchists being repressed in Russia, two new antithetical currents emerged, namely platformism and synthesis anarchism. The former sought to create a coherent group that would push for revolution while the latter were against anything that would resemble a political party. Seeing the victories of the Bolsheviks in the October Revolution and the resulting Russian Civil War, many workers and activists turned to communist parties which grew at the expense of anarchism and other socialist movements. In France and the United States, members of major syndicalist movements such as the General Confederation of Labour and the Industrial Workers of the World left their organisations and joined the Communist International.  In the Spanish Civil War of 1936-39, anarchists and syndicalists (CNT and FAI) once again allied themselves with various currents of leftists. A long tradition of Spanish anarchism led to anarchists playing a pivotal role in the war, and particularly in the Spanish Revolution of 1936. In response to the army rebellion, an anarchist-inspired movement of peasants and workers, supported by armed militias, took control of Barcelona and of large areas of rural Spain, where they collectivised the land. The Soviet Union provided some limited assistance at the beginning of the war, but the result was a bitter fight between communists and other leftists in a series of events known as the May Days, as Joseph Stalin asserted Soviet control of the Republican government, ending in another defeat of anarchists at the hands of the communists.  Post-WWII   By the end of World War II, the anarchist movement had been severely weakened. The 1960s witnessed a revival of anarchism, likely caused by a perceived failure of Marxism-Leninism and tensions built by the Cold War. During this time, anarchism found a presence in other movements critical towards both capitalism and the state such as the anti-nuclear, environmental, and peace movements, the counterculture of the 1960s, and the New Left. It also saw a transition from its previous revolutionary nature to provocative anti-capitalist reformism. Anarchism became associated with punk subculture as exemplified by bands such as Crass and the Sex Pistols. The established feminist tendencies of anarcha-feminism returned with vigour during the second wave of feminism. Black anarchism began to take form at this time and influenced anarchism's move from a Eurocentric demographic. This coincided with its failure to gain traction in Northern Europe and its unprecedented height in Latin America.  Around the turn of the 21st century, anarchism grew in popularity and influence within anti-capitalist, anti-war and anti-globalisation movements. Anarchists became known for their involvement in protests against the World Trade Organization (WTO), the Group of Eight and the World Economic Forum. During the protests, ad hoc leaderless anonymous cadres known as black blocs engaged in rioting, property destruction and violent confrontations with the police. Other organisational tactics pioneered at this time include affinity groups, security culture and the use of decentralised technologies such as the Internet. A significant event of this period was the confrontations at the 1999 Seattle WTO conference. Anarchist ideas have been influential in the development of the Zapatistas in Mexico and the Democratic Federation of Northern Syria, more commonly known as Rojava, a de facto autonomous region in northern Syria.  While having revolutionary aspirations, many forms of anarchism are not confrontational nowadays. Instead, they are trying to build an alternative way of social organization, based on mutual interdependence and voluntary cooperation. Scholar Carissa Honeywell takes the example of Food not Bombs group of collectives, to highlight some features of how anarchist groups work: direct action, working together and in solidarity with those left behind. While doing so, they inform about the rising rates of world hunger suggest a policy to tackle hunger, ranging from de-funding the arms industry to addressing Monsanto seed-saving policies and patents, helping farmers and commodification of food and housing. Honeywell also emphasizes that contemporary anarchists are interested in the flourishing not only of humans, but non-humans and the environment as well. Honeywell argues that escalation of problems such as continuous wars and world poverty show that the current framework not only cannot solve those pressing problems for humanity, but are causal factors as well, resulting in the rejection of representative democracy and the state as a whole.  Thought  Anarchist schools of thought have been generally grouped into two main historical traditions, social anarchism and individualist anarchism, owing to their different origins, values and evolution. The individualist current emphasises negative liberty in opposing restraints upon the free individual, while the social current emphasises positive liberty in aiming to achieve the free potential of society through equality and social ownership. In a chronological sense, anarchism can be segmented by the classical currents of the late 19th century and the post-classical currents (anarcha-feminism, green anarchism, and post-anarchism) developed thereafter.  Beyond the specific factions of anarchist movements which constitute political anarchism lies philosophical anarchism which holds that the state lacks moral legitimacy, without necessarily accepting the imperative of revolution to eliminate it. A component especially of individualist anarchism, philosophical anarchism may tolerate the existence of a minimal state but claims that citizens have no moral obligation to obey government when it conflicts with individual autonomy. Anarchism pays significant attention to moral arguments since ethics have a central role in anarchist philosophy. Anarchism's emphasis on anti-capitalism, egalitarianism, and for the extension of community and individuality sets it apart from anarcho-capitalism and other types of economic libertarianism.  Anarchism is usually placed on the far-left of the political spectrum. Much of its economics and legal philosophy reflect anti-authoritarian, anti-statist, libertarian, and radical interpretations of left-wing and socialist politics such as collectivism, communism, individualism, mutualism, and syndicalism, among other libertarian socialist economic theories. As anarchism does not offer a fixed body of doctrine from a single particular worldview, many anarchist types and traditions exist and varieties of anarchy diverge widely. One reaction against sectarianism within the anarchist milieu was anarchism without adjectives, a call for toleration and unity among anarchists first adopted by Fernando Tarrida del Mármol in 1889 in response to the bitter debates of anarchist theory at the time. Belief in political nihilism has been espoused by anarchists. Despite separation, the various anarchist schools of thought are not seen as distinct entities but rather as tendencies that intermingle and are connected through a set of uniform principles such as individual and local autonomy, mutual aid, network organisation, communal democracy, justified authority and decentralisation.  Classical   Inceptive currents among classical anarchist currents were mutualism and individualism. They were followed by the major currents of social anarchism (collectivist, communist and syndicalist). They differ on organisational and economic aspects of their ideal society.  Mutualism is an 18th-century economic theory that was developed into anarchist theory by Pierre-Joseph Proudhon. Its aims include "abolishing the state", reciprocity, free association, voluntary contract, federation and monetary reform of both credit and currency that would be regulated by a bank of the people. Mutualism has been retrospectively characterised as ideologically situated between individualist and collectivist forms of anarchism. In What Is Property? (1840), Proudhon first characterised his goal as a "third form of society, the synthesis of communism and property." Collectivist anarchism is a revolutionary socialist form of anarchism commonly associated with Mikhail Bakunin. Collectivist anarchists advocate collective ownership of the means of production which is theorised to be achieved through violent revolution and that workers be paid according to time worked, rather than goods being distributed according to need as in communism. Collectivist anarchism arose alongside Marxism but rejected the dictatorship of the proletariat despite the stated Marxist goal of a collectivist stateless society.  Anarcho-communism is a theory of anarchism that advocates a communist society with common ownership of the means of production, held by a federal network of voluntary associations, with production and consumption based on the guiding principle "From each according to his ability, to each according to his need." Anarcho-communism developed from radical socialist currents after the French Revolution but was first formulated as such in the Italian section of the Firs...