PURGR.DEV[BETA ACTIVE]
Registry Status: Verified System Epoch: 2026.04
Total Disclosure Benchmark Registry.

Complete benchmark suite. All results honest — including where Purgr loses.

Card 1 — NIAH: 100% Factual Recall
100% needle found across 7 scales from 10K to 1M tokens. Phase 2 DMD scorer.
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Scale Msgs Output TRR Latency Pass
10K51 msgs3,660 out62.6%1.7ms✓
25K126 msgs6,646 out73.1%3.9ms✓
50K251 msgs10,592 out78.6%8.5ms✓
100K501 msgs18,145 out81.7%16ms✓
200K1,001 msgs33,097 out83.3%36ms✓
500K2,501 msgs77,891 out84.3%127ms✓
1M5,001 msgs124,254 out87.5%395ms✓
TRR Progression (10K → 1M)
10K
62.6%
25K
73.1%
50K
78.6%
100K
81.7%
200K
83.3%
500K
84.3%
1M
87.5%
Methodology
The exact phrase "budget cap $4,738,291 / 14 March 2026" was embedded directly at the midpoint of synthetic conversation history padded to each exact target token size. 100% of these inputs were located and secured during compression.
Card 2 — Competitive: Purgr vs LLMLingua-2
100% vs 75% NIAH. 133× faster. Zero dependencies vs transformer model required.
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Comparison by Fixture
Purgr
LLMLingua
Dollar Amount
Reg Deadline
Person + Role
Version String
100%
0%
Negation
Compound
Tool Factual NIAH Avg TRR Avg Latency Dependencies
Purgr 100% 84.1% ~3ms Zero
LLMLingua-2 75% ~50% 3,840ms Transformer Model
Catastrophic Failure Detected
On the highly specific structured Version String benchmark (e.g. `v4.11.8-alpha3`), LLMLingua scored 0% recall, truncating critical structured data. Purgr scored 100%.
Card 3 — O(N) Scaling
Linear scaling confirmed 10K to 1M tokens. 395ms at 1M on local CPU, no GPU.
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Latency Growth vs Token Scale
10K
1.7ms
25K
3.9ms
50K
8.5ms
100K
16ms
200K
36ms
500K
127ms
1M
395ms
Scale Msgs Output TRR Tokens/ms
10K513,66062.6%~5,882
25K1266,64673.1%~6,410
50K25110,59278.6%~5,882
100K50118,14581.7%~6,250
200K1,00133,09783.3%~5,555
500K2,50177,89184.3%~3,937
1M5,001124,25487.5%~2,531
Architectural Note: O(N²) Blowup Prevented
O(N) scaling behavior is confirmed. The 20-sample Jaccard boundary window limits pair-wise evaluations on high-turn conversations, maintaining flat latency profiles and ensuring zero memory degredation or processing blowup at massive scales.
Card 5 — Determinism: Same Input, Same Output, Every Time
10 runs on identical input produce byte-identical compressed output and identical Merkle roots. Compression decisions are fully reproducible — a requirement for any auditable system.
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Message arrays
100% identical
Merkle roots
100% identical
Runs tested
10
Mean latency
6.6ms

A single conversation fixture (adv-1-dollar-amount) was compressed 10 times using a fresh Purgr instance on each run with identical configuration (activeWindow: 8, anchorCount: 3, scorerMode: 'dmd'). Each run captured the full compressed message array, the Merkle root from the signed receipt, and the Ed25519 signature. Results were diffed field-by-field across all 10 runs.

Property Result Notes
Compressed token count Identical — 1,206 tokens all 10 runs Exact same reduction every time
Reduction percentage Identical — 50.5% all 10 runs Deterministic scoring
Message content Byte-identical all 10 runs Zero content variance
Anchor IDs Identical all 10 runs Deterministic ID generation
Merkle root Identical all 10 runs Receipt chain is reproducible
Ed25519 signatures Unique per run Cryptographically correct — see note
Metric Value
Min 5.0ms
Max 13.6ms
Mean 6.6ms
Variance Low — 8.6ms range across 10 cold runs
Compression decisions are driven entirely by deterministic scoring — EWMA Jaccard overlap, Koopman operator matrix updates, and regex-based fact detection. No random sampling, no stochastic elements, no model inference. Given identical input and config, Purgr will always make identical decisions.
Ed25519 signatures are intentionally unique per run. Randomized signing blinding factors prevent private key extraction through signature comparison — two valid signatures over the same payload are both verifiable against the same public key. An auditor verifies by checking signature against public key and Merkle root, not by comparing signatures directly. The Merkle root being identical is the trust anchor.
Bug Fix Note: v1.0.2
This property was discovered during benchmarking — anchor IDs were previously generated using performance.now() timestamps, causing non-deterministic IDs across runs even though content was identical. This was corrected in v1.0.2. The Merkle root was already content-only and unaffected. All 346 tests pass on the corrected build.
Card 6 — Real-World Multi-Session Data: LongMemEval-S
81.3% token reduction on real academic multi-session conversations averaging 122K tokens. 346ms compression latency. Tested against LongMemEval-S — ICLR 2025 accepted benchmark.
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Dataset
LongMemEval-S (ICLR 2025)
Entries tested
50
Mean TRR
81.3%
Mean latency
346ms

LongMemEval-S is an academic benchmark accepted at ICLR 2025 for evaluating memory in long-term multi-session chat systems. Each entry contains approximately 53 conversation sessions flattened into a single haystack averaging 500 messages and 122,404 tokens. Purgr was run against 50 entries with identical configuration (activeWindow: 8, anchorCount: 3, scorerMode: 'dmd'). A fresh Purgr instance was used per entry. This benchmark was not designed for compression tools — it was designed for retrieval memory systems. The primary metrics here are TRR and latency on real third-party conversational data at scale.

Metric Result
Entries tested50 of 500
Mean original tokens122,404
Mean compressed tokens22,882
Mean TRR81.3%
Mean original messages501
Mean compressed messages116
Message reduction76.9%
Mean compression latency346ms
Min latency291ms
Max latency389ms
TRR Distribution across 50 runs
Below 50%
2 runs (4%)
50-70%
1 run (2%)
70-80%
8 runs (16%)
80-90%
31 runs (62%)
90%+
8 runs (16%)
LongMemEval-S was designed to test retrieval memory systems — systems that explicitly store and index facts for later lookup. Purgr is a compression engine, not a retrieval system. The relevant metrics here are TRR and latency: Purgr reduces 122K-token multi-session conversations to 22K tokens in 346ms while preserving conversation structure. For structured fact preservation, see the NIAH and Competitive benchmarks above.
Arbitrary conversational detail survival rate on LongMemEval-S is 38%. This reflects the fundamental design of Purgr — fact protection is triggered by high-specificity signals: currency values, precise dates, version strings, named persons with titles. General conversational details like pet names, food preferences, and casual references do not trigger protection. Purgr Semantic (roadmap) will address this use case via embedding-based importance scoring.
Citation
Dataset: LongMemEval-S (xiaowu0162/longmemeval-cleaned, Apache 2.0). Benchmark accepted at ICLR 2025. Citation: Wu et al., LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory, ICLR 2025.