Morning Singularity Digest - 2026-06-11

Context

The best-benchmarked open-source AI memory system.

What's new

The best-benchmarked open-source AI memory system.

Key details

• Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls.
• MemPalace has no other official websites.
• The only official sources are this GitHub repository, the PyPI package, and the docs at mempalaceofficial.com.
• Any other domain (including .tech, .net, or other .com variants) is an impostor and may distribute malware.

Results & evidence

• Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls.
• Important Claude Code sessions expire in 30 days without auto-save hooks wired.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

The agent harness performance optimization system.

What's new

Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.

Key details

• Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
• Language: English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe | Русский | Tiếng Việt | ไทย | Deutsch | Español 211.9K+ stars | 32.5K+ forks | 230+ contributors | 12+ language ecosystems | Cross-harness agent workflows Language / 语言 / 語言 / Dil /...
• Built from real-world multi-harness engineering workflows.
• A complete system: skills, instincts, memory optimization, continuous learning, security scanning, and research-first development.

Results & evidence

• Language: English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe | Русский | Tiếng Việt | ไทย | Deutsch | Español 211.9K+ stars | 32.5K+ forks | 230+ contributors | 12+ language ecosystems | Cross-harness agent workflows Language / 语言 / 語言 / Dil /...
• Production-ready agents, skills, hooks, rules, MCP configurations, and legacy command shims evolved over 10+ months of intensive daily use building real products.
• ECC v2.0.0 adds the public Hermes operator story on top of that reusable layer: start with the Hermes setup guide, then review the 2.0.0 release notes and cross-harness architecture.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

The main challenge is that most studies treat the poetic appreciation task as a general-domain problem, neglecting the distinctive features of poetic appreciation, while high-quality and domain-specific datasets are extremely limited.

What's new

We then propose a domain-specialized LLM, called PoetryQwen, by applying Low-Rank Adaptation (LoRA) to fine-tune the Qwen2.5-14B model.

Key details

• However, domain-specific research on precise translation and affective-semantic understanding of classical poetry remains limited.
• The main challenge is that most studies treat the poetic appreciation task as a general-domain problem, neglecting the distinctive features of poetic appreciation, while high-quality and domain-specific datasets are extremely limited.
• To address this limitation, we decompose the task into three subtasks: term interpretation, semantic interpretation, and emotional inference.
• Based on multiple open-source datasets, we perform data cleansing and alignment to construct the Classical Chinese Poetry Instruction Pair Dataset (CCPoetry-49K), which comprises 49,404 high-quality instruction-response pairs explicitly optimized for this d...

Results & evidence

• arXiv:2606.12392v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry.
• Based on multiple open-source datasets, we perform data cleansing and alignment to construct the Classical Chinese Poetry Instruction Pair Dataset (CCPoetry-49K), which comprises 49,404 high-quality instruction-response pairs explicitly optimized for this d...
• We then propose a domain-specialized LLM, called PoetryQwen, by applying Low-Rank Adaptation (LoRA) to fine-tune the Qwen2.5-14B model.

Limitations / unknowns

• However, domain-specific research on precise translation and affective-semantic understanding of classical poetry remains limited.
• The main challenge is that most studies treat the poetic appreciation task as a general-domain problem, neglecting the distinctive features of poetic appreciation, while high-quality and domain-specific datasets are extremely limited.
• To address this limitation, we decompose the task into three subtasks: term interpretation, semantic interpretation, and emotional inference.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

arXiv:2606.11215v1 Announce Type: cross Abstract: Large Language Model (LLM) usage in recent years has become increasingly widespread in the Artificial Intelligence in Education (AIED) community.

What's new

To address this gap, we first conducted a literature review of all papers published as part of the AIED 2025 conference proceedings, determining if and how computational or environmental costs of LLMs are reported.

Key details

• While LLMs offer unique avenues for learners and educators, using LLMs comes with computational and environmental costs.
• These costs are mostly hidden due to a lack of standardised procedures to measure and report these impacts.
• To address this gap, we first conducted a literature review of all papers published as part of the AIED 2025 conference proceedings, determining if and how computational or environmental costs of LLMs are reported.
• Most projects use LLMs, but few report computational resources used and almost none discuss environmental impacts of LLMs as an ethical concern.

Results & evidence

• arXiv:2606.11215v1 Announce Type: cross Abstract: Large Language Model (LLM) usage in recent years has become increasingly widespread in the Artificial Intelligence in Education (AIED) community.
• To address this gap, we first conducted a literature review of all papers published as part of the AIED 2025 conference proceedings, determining if and how computational or environmental costs of LLMs are reported.
• Computer Science > Computers and Society [Submitted on 3 May 2026] Title:The Environmental Cost of LLMs in AIED: Reporting and Practices View PDF HTML (experimental)Abstract:Large Language Model (LLM) usage in recent years has become increasingly widespread...

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Helm AI Kernel, a fail-closed execution firewall for AI agents

What's new

Helm AI Kernel, a fail-closed execution firewall for AI agents

Key details

• Helm AI Kernel, a fail-closed execution firewall for AI agents

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

The open-source app everyone uses to manage agents at work Quickstart · Docs · GitHub · Discord · Twitter · Website full-tour.webm Open-source orchestration for teams of AI agents.

What's new

The open-source app everyone uses to manage agents at work Quickstart · Docs · GitHub · Discord · Twitter · Website full-tour.webm Open-source orchestration for teams of AI agents.

Key details

• If OpenClaw is an employee, Paperclip is the company.
• Paperclip is a Node.js server and React UI that orchestrates a team of AI agents to run a business.
• Bring your own agents, assign goals, and track work and costs from one dashboard.
• Under the hood: org charts, budgets, governance, goal alignment, and agent coordination.

Results & evidence

• | Step | Example | | |---|---|---| | 01 | Define the goal | "Build the #1 AI note-taking app to $1M MRR." | | 02 | Hire the team | CEO, CTO, engineers, designers, marketers — any bot, any provider.
• | | 03 | Approve and run | Review strategy.
• | - ✅ You want to build autonomous AI companies - ✅ You coordinate many different agents (OpenClaw, Codex, Claude, Cursor) toward a common goal - ✅ You have 20 simultaneous Claude Code terminals open and lose track of what everyone is doing - ✅ You want age...

Limitations / unknowns

• When they hit the limit, they stop.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

The main challenge is that most studies treat the poetic appreciation task as a general-domain problem, neglecting the distinctive features of poetic appreciation, while high-quality and domain-specific datasets are extremely limited.

What's new

We then propose a domain-specialized LLM, called PoetryQwen, by applying Low-Rank Adaptation (LoRA) to fine-tune the Qwen2.5-14B model.

Key details

• However, domain-specific research on precise translation and affective-semantic understanding of classical poetry remains limited.
• The main challenge is that most studies treat the poetic appreciation task as a general-domain problem, neglecting the distinctive features of poetic appreciation, while high-quality and domain-specific datasets are extremely limited.
• To address this limitation, we decompose the task into three subtasks: term interpretation, semantic interpretation, and emotional inference.
• Based on multiple open-source datasets, we perform data cleansing and alignment to construct the Classical Chinese Poetry Instruction Pair Dataset (CCPoetry-49K), which comprises 49,404 high-quality instruction-response pairs explicitly optimized for this d...

Results & evidence

• arXiv:2606.12392v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry.
• Based on multiple open-source datasets, we perform data cleansing and alignment to construct the Classical Chinese Poetry Instruction Pair Dataset (CCPoetry-49K), which comprises 49,404 high-quality instruction-response pairs explicitly optimized for this d...
• We then propose a domain-specialized LLM, called PoetryQwen, by applying Low-Rank Adaptation (LoRA) to fine-tune the Qwen2.5-14B model.

Limitations / unknowns

• However, domain-specific research on precise translation and affective-semantic understanding of classical poetry remains limited.
• The main challenge is that most studies treat the poetic appreciation task as a general-domain problem, neglecting the distinctive features of poetic appreciation, while high-quality and domain-specific datasets are extremely limited.
• To address this limitation, we decompose the task into three subtasks: term interpretation, semantic interpretation, and emotional inference.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

arXiv:2606.11215v1 Announce Type: cross Abstract: Large Language Model (LLM) usage in recent years has become increasingly widespread in the Artificial Intelligence in Education (AIED) community.

What's new

To address this gap, we first conducted a literature review of all papers published as part of the AIED 2025 conference proceedings, determining if and how computational or environmental costs of LLMs are reported.

Key details

• While LLMs offer unique avenues for learners and educators, using LLMs comes with computational and environmental costs.
• These costs are mostly hidden due to a lack of standardised procedures to measure and report these impacts.
• To address this gap, we first conducted a literature review of all papers published as part of the AIED 2025 conference proceedings, determining if and how computational or environmental costs of LLMs are reported.
• Most projects use LLMs, but few report computational resources used and almost none discuss environmental impacts of LLMs as an ethical concern.

Results & evidence

• arXiv:2606.11215v1 Announce Type: cross Abstract: Large Language Model (LLM) usage in recent years has become increasingly widespread in the Artificial Intelligence in Education (AIED) community.
• To address this gap, we first conducted a literature review of all papers published as part of the AIED 2025 conference proceedings, determining if and how computational or environmental costs of LLMs are reported.
• Computer Science > Computers and Society [Submitted on 3 May 2026] Title:The Environmental Cost of LLMs in AIED: Reporting and Practices View PDF HTML (experimental)Abstract:Large Language Model (LLM) usage in recent years has become increasingly widespread...

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

The main challenge is that most studies treat the poetic appreciation task as a general-domain problem, neglecting the distinctive features of poetic appreciation, while high-quality and domain-specific datasets are extremely limited.

What's new

We then propose a domain-specialized LLM, called PoetryQwen, by applying Low-Rank Adaptation (LoRA) to fine-tune the Qwen2.5-14B model.

Key details

• However, domain-specific research on precise translation and affective-semantic understanding of classical poetry remains limited.
• The main challenge is that most studies treat the poetic appreciation task as a general-domain problem, neglecting the distinctive features of poetic appreciation, while high-quality and domain-specific datasets are extremely limited.
• To address this limitation, we decompose the task into three subtasks: term interpretation, semantic interpretation, and emotional inference.
• Based on multiple open-source datasets, we perform data cleansing and alignment to construct the Classical Chinese Poetry Instruction Pair Dataset (CCPoetry-49K), which comprises 49,404 high-quality instruction-response pairs explicitly optimized for this d...

Results & evidence

• arXiv:2606.12392v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry.
• Based on multiple open-source datasets, we perform data cleansing and alignment to construct the Classical Chinese Poetry Instruction Pair Dataset (CCPoetry-49K), which comprises 49,404 high-quality instruction-response pairs explicitly optimized for this d...
• We then propose a domain-specialized LLM, called PoetryQwen, by applying Low-Rank Adaptation (LoRA) to fine-tune the Qwen2.5-14B model.

Limitations / unknowns

• However, domain-specific research on precise translation and affective-semantic understanding of classical poetry remains limited.
• The main challenge is that most studies treat the poetic appreciation task as a general-domain problem, neglecting the distinctive features of poetic appreciation, while high-quality and domain-specific datasets are extremely limited.
• To address this limitation, we decompose the task into three subtasks: term interpretation, semantic interpretation, and emotional inference.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

arXiv:2606.11215v1 Announce Type: cross Abstract: Large Language Model (LLM) usage in recent years has become increasingly widespread in the Artificial Intelligence in Education (AIED) community.

What's new

To address this gap, we first conducted a literature review of all papers published as part of the AIED 2025 conference proceedings, determining if and how computational or environmental costs of LLMs are reported.

Key details

• While LLMs offer unique avenues for learners and educators, using LLMs comes with computational and environmental costs.
• These costs are mostly hidden due to a lack of standardised procedures to measure and report these impacts.
• To address this gap, we first conducted a literature review of all papers published as part of the AIED 2025 conference proceedings, determining if and how computational or environmental costs of LLMs are reported.
• Most projects use LLMs, but few report computational resources used and almost none discuss environmental impacts of LLMs as an ethical concern.

Results & evidence

• arXiv:2606.11215v1 Announce Type: cross Abstract: Large Language Model (LLM) usage in recent years has become increasingly widespread in the Artificial Intelligence in Education (AIED) community.
• To address this gap, we first conducted a literature review of all papers published as part of the AIED 2025 conference proceedings, determining if and how computational or environmental costs of LLMs are reported.
• Computer Science > Computers and Society [Submitted on 3 May 2026] Title:The Environmental Cost of LLMs in AIED: Reporting and Practices View PDF HTML (experimental)Abstract:Large Language Model (LLM) usage in recent years has become increasingly widespread...

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

arXiv:2606.10725v2 Announce Type: replace Abstract: Background.

What's new

arXiv:2606.10725v2 Announce Type: replace Abstract: Background.

Key details

• Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinant of prognosis.
• Established AF risk scores rely on factors (older age, hypertension) nearly ubiquitous among patients with cardiovascular disease (CVD), offering limited stratification in this high-risk group.
• Most target long-term (5-10 year) rather than medium-term prediction.
• We developed interpretable ML models predicting AF risk over a 24-month and entire follow-up horizon in CVD patients using routinely collected hospital data.

Results & evidence

• arXiv:2606.10725v2 Announce Type: replace Abstract: Background.
• Most target long-term (5-10 year) rather than medium-term prediction.
• We developed interpretable ML models predicting AF risk over a 24-month and entire follow-up horizon in CVD patients using routinely collected hospital data.

Limitations / unknowns

• Established AF risk scores rely on factors (older age, hypertension) nearly ubiquitous among patients with cardiovascular disease (CVD), offering limited stratification in this high-risk group.
• We developed interpretable ML models predicting AF risk over a 24-month and entire follow-up horizon in CVD patients using routinely collected hospital data.
• Using LightAutoML we built a full model (73 features), a simple model (reduced subset), and a linear model for a bedside risk score.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

For file submission/navigation questions, see Navigation and file context.

What's new

Windows users can jump to the PowerShell-first Windows install and release quickstart.

Key details

• github.com/code-yeongyu/lazycodex github.com/Yeachan-Heo/gajae-code Join the Discords: ultraworkers discord · gajae-code discord Important Claw Code is not the serious production project here.
• This repository is closer to a museum exhibit than a product pitch, a crustacean-run artifact kept alive by clawed gajaes, swept and labeled by agents, and automatically maintained according to the harnesses above.
• As already described in the project philosophy, this is not meant to be hand-operated like a normal product repo.
• It is an agent-managed exhibit: the harnesses plan, execute, verify, label, and preserve the artifact while the crabs keep the tank running.

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

We investigate how human guidance of agentic AI can improve multimodal clinical prediction, presenting our approach to all three AgentDS Healthcare benchmark challenges: 30-day hospital readmission prediction (Macro-F1 = 0.8986), emergency department cost f...

What's new

arXiv:2602.19502v2 Announce Type: replace Abstract: Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely automated approaches struggle to provide.

Key details

• We investigate how human guidance of agentic AI can improve multimodal clinical prediction, presenting our approach to all three AgentDS Healthcare benchmark challenges: 30-day hospital readmission prediction (Macro-F1 = 0.8986), emergency department cost f...
• Across these tasks, human analysts directed the agentic workflow at key decision points, multimodal feature engineering from clinical notes, scanned PDF billing receipts, and time-series vital signs; task-appropriate model selection; and clinically informed...
• Our approach ranked 5th overall in the healthcare domain, with a 3rd-place finish on the discharge readiness task.
• Ablation studies reveal that human-guided decisions compounded to a cumulative gain of +0.065 F1 over automated baselines, with multimodal feature extraction contributing the largest single improvement (+0.041 F1).

Results & evidence

• arXiv:2602.19502v2 Announce Type: replace Abstract: Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely automated approaches struggle to provide.
• We investigate how human guidance of agentic AI can improve multimodal clinical prediction, presenting our approach to all three AgentDS Healthcare benchmark challenges: 30-day hospital readmission prediction (Macro-F1 = 0.8986), emergency department cost f...
• Ablation studies reveal that human-guided decisions compounded to a cumulative gain of +0.065 F1 over automated baselines, with multimodal feature extraction contributing the largest single improvement (+0.041 F1).

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Show HN: Dupehound – find the code your agent wrote twice (no AI required)

What's new

Show HN: Dupehound – find the code your agent wrote twice (no AI required)

Key details

• Show HN: Dupehound – find the code your agent wrote twice (no AI required)

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Show HN: Flightdeck – self-hosted observability and control for AI agents

What's new

Show HN: Flightdeck – self-hosted observability and control for AI agents

Key details

• Show HN: Flightdeck – self-hosted observability and control for AI agents

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Hi I have been interested in stock investing and analyzing companies using methods described in Peter Lynch books.

I thought that maybe AI could make the whole process faster.

What's new

Hi I have been interested in stock investing and analyzing companies using methods described in Peter Lynch books.

I thought that maybe AI could make the whole process faster.

Key details

• So I built an app I would like to share with you and hear your feedback if possible :)

  I also think about creating agent skill for it so no one is dependent on the web app I built.

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler

What's new

Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler

Key details

• Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.