AnonyJcy/dsh-j-space

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J-Space Cognition Suite SV1 native Agent Preset & standalone Cordis plugin for DeepSeek Harness (DSH).
Bringing internal thought representations, externalized workspace ledgers (.jspace/), and adaptive verification to unlock full LLM reasoning potential.

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Compatibility: adapted to DSH 0.1.6 dsh-workflow-ptc and DSH 0.1.5 dsh-persona schema (config.prefix / config.suffix).

🌟 Overview

dsh-j-space integrates the J-Space Cognition Suite (SV1 release, continuing V3.7 evolution) into DeepSeek Harness as a native Agent Preset.

Unlike traditional flat prompt injections, this plugin provides full agent scope isolation, multi-tier reasoning routes, externalized workspace ledgers (.jspace/), and adaptive verification across any compatible LLM model (DeepSeek, Claude, GPT, etc.).

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📸 Screenshots & Preview

DSH Preset Selection (Zero-Config)	J-Space Cognition Session in Action

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📊 Empirical Capability Realization Report

Full evaluation reports by the original author:

• Current Benchmark Report: GLM-5.3-Flash-J-Space-Capability-Realization-Report
• Earlier Comparative Report (Preserved Archive): DeepSeek-V4-J-Space-Capability-Realization-Report

🔬 Benchmark 1: GLM-5.3-Flash Evaluation (Latest Report)

1. Main Benchmark Table (Accuracy Comparison)

Benchmark	GLM-5.3-Flash (Baseline)	GLM-5.3-Flash + J-Space V3.7/SV1†	GLM-5.3	Opus-5	Fable 5.1
HLE (w/ tools)	55.3	59.2	62.5	64.7	65.0
Terminal Bench 2.1	84.3	88.8	88.2	*89.1	*91.4
DeepSWE v1.1	63.4	68.0	66.9	68.8	67.4
Agents' Last Exam	26.3	30.5	28.5	31.6	—
AutomationBench (Public)	48.8	51.1	48.2	50.3	—

* Note: Terminal Bench 2.1 figures for Opus-5 and Fable 5.1 are independently measured by a third party; no official entries exist.
\† Estimated, based on limited controlled experiments.

2. Speed and Token Efficiency Table (GAIA Controlled Pair)

Metric	Factor / Improvement
Speed	1.87×
Token Efficiency	1.41×

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🔬 Benchmark 2: DeepSeek-V4-Flash Evaluation (Historical Archive)

• Base Model: DeepSeek-V4-Flash-Vision-Exp
• Harness: DeepSeek Harness (Standard Mode)
• Methodology: Rigorous A/B Testing with and without J-Space on authoritative benchmark subsets and same-type mini-sets (Terminal-Bench 2.1: 20 medium / 10 hard; DeepSWE: 10 TypeScript / 10 Python / 10 Go / 2 JavaScript / 2 Rust; GAIA: level 1 / level 3, etc.), with identical model, environment, and sampling — only the J-Space toggle differs.
• Evaluation Dimensions: ① Accuracy / Pass Rate; ② Wall-clock & Token Efficiency.

1. Main Benchmark Table (Accuracy Comparison)

Benchmark	DeepSeek V4-Flash (Baseline)	DeepSeek V4-Flash + J-Space V3.7	GLM-5.3	Kimi-K3	Opus-4.8	Fable 5 (w/ fallback)
HLE (w/o tools)	*37.8	37.8	—	43.5	49.8	53.3
HLE (w/ tools)	*51.5	51.9	62.5	56.0	57.9	63.0
Terminal Bench 2.1	83.9	85.5	88.2	88.3	85.0	88.0
NL2Repo	57.7	60.4	58.0	58.0	69.7	—
CyberGym	75.3	77.8	84.5	80.0	78.3	83.1
DeepSWE	59.3	61.8	66.9	67.5	58.0	70.0
Toolathlon-Verified	75.9	77.4	73.0	76.5	76.2	77.9
Agents' Last Exam	27.3	28.3	28.5	27.6	25.7	23.8
AutomationBench (Public)	25.7	27.6	48.2	30.8	27.2	29.1
⭐ Average Score	56.99	58.61	64.54	60.96	58.33	62.13

* Note: HLE scores were not disclosed and follow DeepSeek V4-Flash-0731. The average covers the 7 rows where all six columns have values.

2. Speed and Token Efficiency Table

Benchmark	Wall-clock τ	Speedup	Output Tokens	Total Tokens	Score per Unit Time	Cost per Successful Task
HLE (w/o tools)	*1.02	−2%	−10%	+5%	0.98×	+5%
HLE (w/ tools)	0.88	+14%	−22%	+3%	1.15×	+2%
Terminal Bench 2.1	0.79	+27%	−28%	−3%	1.29×	−5%
DeepSWE	0.78	+28%	−28%	−3%	1.34×	−7%
Toolathlon-Verified	0.86	+16%	−25%	+2%	1.19×	+0%
AutomationBench (Public)	0.76	+32%	−31%	−5%	1.41×	−12%

** Note: For HLE (w/o tools), τ=1.02 is intentionally positive (i.e. slower) because on single-turn tasks without tools, injecting the full skill entry is net overhead. On long-horizon and multi-turn coding/agentic benchmarks (e.g. Terminal Bench, DeepSWE, AutomationBench), J-Space delivers +14% ~ +32% faster execution, cuts 28%~31% of output token redundancy, and boosts score per unit time by 1.15× ~ 1.41×.*

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🚀 Installation & Deployment

Method 1: Install from npm / pnpm (Official Registry)

# via npm
npm install -D @anonyjcy/dsh-j-space

# via pnpm
pnpm add -D @anonyjcy/dsh-j-space

# Deploy preset to ~/.dsh/.agent-presets/j-space
npx @anonyjcy/dsh-j-space install

Method 2: Direct Clone & Install (Local Use)

git clone https://github.com/AnonyJcy/dsh-j-space.git
cd dsh-j-space

# Deploy J-Space preset to ~/.dsh/.agent-presets/j-space/
node bin/cli.js install

# Check status
node bin/cli.js status

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💡 Usage

1. In DeepSeek Harness Web UI

1. Create a new Session.
2. Select J-Space Cognition Suite in the Agent Preset dropdown.
3. Pick any compatible model (deepseek-chat, deepseek-reasoner, etc.) and start your task.

2. In DeepSeek Harness CLI

dsh --preset j-space "Analyze this architecture and implement feature X"

3. In Cordis Composition (cordis.yml)

- id: j-space-plugin
  name: '@anonyjcy/dsh-j-space'
  config:
    autoDeploy: true

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🧩 Architecture & Data Flow

flowchart TD
    A[New Session] --> B[Select j-space Preset]
    B --> C[Preset Discovery: AgentPresets.list]
    C --> D[Preset Mount: AgentPresets.mount]
    D --> E[Agent Scope]
    E --> F1[Persona: J-Space SV1 Architecture]
    E --> F2[Tools: Full Coding & Reasoning Tools]
    E --> F3[Skill Filesystem: Mounted skills/j-space/]
    E --> F4[J-Space Suite: SKILL.md, 13 modules, 7 references, controller & adapters]
    E --> G[Session Model Route: Any Model]
    G --> H[Agent Executes J-Space Cognition Loop]
    H --> I[Task Workspace: Managed .jspace/ Ledger & Control State]

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🛠️ CLI Commands

node bin/cli.js install    # Deploy J-Space preset to ~/.dsh/.agent-presets/j-space
node bin/cli.js uninstall  # Cleanly remove J-Space preset
node bin/cli.js verify     # Verify integrity of installed preset files
node bin/cli.js status     # Display current installation status

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📄 License

MIT License. See LICENSE and THIRD_PARTY_NOTICES.md.

Maintenance

See CHANGELOG.md for release notes.