Signal 10.0Novelty 6.2Impact 8.2Confidence 7.0Actionability 6.5
Summary: The agent harness performance optimization system.
What happened: The agent harness performance optimization system.
Why it matters: The agent harness performance optimization system.
What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
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 Warning Official sources only.
Install ECC only from verified channels: the GitHub repository github.com/affaan-m/ECC, the npm packages ecc-universal and ecc-agentshield, the GitHub App, the plugin slug ecc@ecc, and the project website ecc.tools.
Third-party re-uploads and unofficial mirrors are not maintained or reviewed by the project and may contain malware.
Results & evidence
211.9K+ stars | 32.5K+ forks | 230+ contributors | 12+ language ecosystems | Cross-harness agent workflows Language / 语言 / 語言 / Dil / Язык / Ngôn ngữ / Idioma English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe | Русский | Tiếng Việt | ไทย | Deu...
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.
Signal 10.0Novelty 6.2Impact 7.7Confidence 7.0Actionability 6.5
Summary: 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 happened: 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.
Why it matters: 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.
What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
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.
Signal 9.4Novelty 4.0Impact 2.0Confidence 8.7Actionability 6.5
Summary: arXiv:2504.02885v2 Announce Type: replace Abstract: Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision.
What happened: We introduce a perception-driven long reasoning process that precedes report generation and incorporates radiology-specific knowledge as guidance.
Why it matters: Our experiments demonstrate that Med-R2 effectively enhances the capability of pathological features perception and diagnosis accuracy for MRG via fine-tuned LVLMs.
What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
arXiv:2504.02885v2 Announce Type: replace Abstract: Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision support.
What's new
Firstly, direct SFT enables LVLMs to generate medical reports directly without an intermediate thinking process of pathological feature perception and diagnostic reasoning.
Key details
Large vision-language models (LVLMs) hold great promise for automated MRG due to their fine-grained image-text alignment and advanced text-generation capabilities.
Currently, state-of-the-art MRGs primarily focus on adapting pre-trained LVLMs with direct supervised fine-tuning (SFT), a fine-tuning strategy with medical image-report pairs.
However, several factors limit the performance of these LVLMs.
Firstly, direct SFT enables LVLMs to generate medical reports directly without an intermediate thinking process of pathological feature perception and diagnostic reasoning.
Results & evidence
arXiv:2504.02885v2 Announce Type: replace Abstract: Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision support.
Computer Science > Computation and Language [Submitted on 2 Apr 2025 (v1), last revised 18 Jun 2026 (this version, v2)] Title:Med-R2: Perception and Reflection-driven Complex Reasoning for Medical Report Generation View PDF HTML (experimental)Abstract:Autom...
Submission history From: Hao Wang [view email][v1] Wed, 2 Apr 2025 08:18:54 UTC (1,248 KB) [v2] Thu, 18 Jun 2026 12:55:35 UTC (740 KB) References & Citations Loading...
Limitations / unknowns
However, several factors limit the performance of these LVLMs.
This causes a potential failure to perceive pathological features and thus leads to misdiagnosis.
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.
Signal 9.4Novelty 4.0Impact 2.0Confidence 8.7Actionability 6.5
Summary: arXiv:2602.13139v4 Announce Type: replace Abstract: Language identification (LID) is an essential step in building high-quality multilingual datasets from web data.
What happened: arXiv:2602.13139v4 Announce Type: replace Abstract: Language identification (LID) is an essential step in building high-quality multilingual datasets from web data.
Why it matters: We find that ensemble approaches improve precision but also substantially reduce coverage for low-resource languages.
What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
In this work we extend the OpenLID classifier by adding more training data, merging problematic language variant clusters, and introducing a special label for marking noise.
What's new
During development, we focus on three groups of closely related languages (Bosnian, Croatian, and Serbian; Romance varieties of Northern Italy and Southern France; and Scandinavian languages) and contribute new evaluation datasets where existing ones are in...
Key details
Existing LID tools (such as OpenLID or GlotLID) often struggle to identify closely related languages and to distinguish valid natural language from noise, which contaminates language-specific subsets, especially for low-resource languages.
In this work we extend the OpenLID classifier by adding more training data, merging problematic language variant clusters, and introducing a special label for marking noise.
We call this extended system OpenLID-v3 and evaluate it against GlotLID on multiple benchmarks.
During development, we focus on three groups of closely related languages (Bosnian, Croatian, and Serbian; Romance varieties of Northern Italy and Southern France; and Scandinavian languages) and contribute new evaluation datasets where existing ones are in...
Results & evidence
arXiv:2602.13139v4 Announce Type: replace Abstract: Language identification (LID) is an essential step in building high-quality multilingual datasets from web data.
Computer Science > Computation and Language [Submitted on 13 Feb 2026 (v1), last revised 18 Jun 2026 (this version, v4)] Title:OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report View PDF HTML (experimental...
Submission history From: Mariia Fedorova [view email][v1] Fri, 13 Feb 2026 17:47:08 UTC (63 KB) [v2] Mon, 23 Feb 2026 17:38:41 UTC (69 KB) [v3] Tue, 16 Jun 2026 11:13:12 UTC (70 KB) [v4] Thu, 18 Jun 2026 11:51:21 UTC (70 KB) References & Citations Loading...
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.
Signal 8.4Novelty 5.1Impact 2.4Confidence 7.5Actionability 5.2
Summary: A standalone MCP tool that catches AI-agent scope creep.
What happened: A standalone MCP tool that catches AI-agent scope creep.
Why it matters: A standalone MCP tool that catches AI-agent scope creep.
What to do: Track for corroboration and benchmark data before adopting.
Deep
Context
A standalone MCP tool that catches AI-agent scope creep.
What's new
A standalone MCP tool that catches AI-agent scope creep.
Key details
You give it the prompt you gave your coding agent, and the diff it produced.
Overreach tells you whether the diff stayed inside what the prompt asked for — or whether the agent quietly added an endpoint, a dependency, an env var, or a cron job that you never asked for.
"turns out my ai assistant had been extremely making product decisions without me" - Node.js 18+ — nodejs.org.
- Git — required for the pre-commit hook and git diffpiping.
Results & evidence
"turns out my ai assistant had been extremely making product decisions without me" - Node.js 18+ — nodejs.org.
Exits 1 with a HIGH scope-creep finding (the demo prompt asks for a login form; the diff smuggles in Stripe, an env var, an endpoint, and a cron job).
- Stage 1 — Scope extraction (LLM).
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.
What Changed Overnight
~1 min
New: SAGE-OPD: Selective Agent-Guided Intervention for Multi-Turn On-Policy Distillation
Removed: Probe-and-Refine Tuning of Repository Guidance for Coding Agents (fell below rank threshold)
What to do now:
Validate with one small internal benchmark and compare against your current baseline this week.
Track for corroboration and benchmark data before adopting.
Signal 10.0Novelty 6.2Impact 7.7Confidence 7.0Actionability 6.5
Summary: 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 happened: 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.
Why it matters: 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.
What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
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.
Signal 9.4Novelty 4.0Impact 2.0Confidence 8.7Actionability 6.5
Summary: arXiv:2504.02885v2 Announce Type: replace Abstract: Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision.
What happened: We introduce a perception-driven long reasoning process that precedes report generation and incorporates radiology-specific knowledge as guidance.
Why it matters: Our experiments demonstrate that Med-R2 effectively enhances the capability of pathological features perception and diagnosis accuracy for MRG via fine-tuned LVLMs.
What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
arXiv:2504.02885v2 Announce Type: replace Abstract: Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision support.
What's new
Firstly, direct SFT enables LVLMs to generate medical reports directly without an intermediate thinking process of pathological feature perception and diagnostic reasoning.
Key details
Large vision-language models (LVLMs) hold great promise for automated MRG due to their fine-grained image-text alignment and advanced text-generation capabilities.
Currently, state-of-the-art MRGs primarily focus on adapting pre-trained LVLMs with direct supervised fine-tuning (SFT), a fine-tuning strategy with medical image-report pairs.
However, several factors limit the performance of these LVLMs.
Firstly, direct SFT enables LVLMs to generate medical reports directly without an intermediate thinking process of pathological feature perception and diagnostic reasoning.
Results & evidence
arXiv:2504.02885v2 Announce Type: replace Abstract: Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision support.
Computer Science > Computation and Language [Submitted on 2 Apr 2025 (v1), last revised 18 Jun 2026 (this version, v2)] Title:Med-R2: Perception and Reflection-driven Complex Reasoning for Medical Report Generation View PDF HTML (experimental)Abstract:Autom...
Submission history From: Hao Wang [view email][v1] Wed, 2 Apr 2025 08:18:54 UTC (1,248 KB) [v2] Thu, 18 Jun 2026 12:55:35 UTC (740 KB) References & Citations Loading...
Limitations / unknowns
However, several factors limit the performance of these LVLMs.
This causes a potential failure to perceive pathological features and thus leads to misdiagnosis.
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.
Signal 9.4Novelty 4.0Impact 2.0Confidence 8.7Actionability 6.5
Summary: arXiv:2602.13139v4 Announce Type: replace Abstract: Language identification (LID) is an essential step in building high-quality multilingual datasets from web data.
What happened: arXiv:2602.13139v4 Announce Type: replace Abstract: Language identification (LID) is an essential step in building high-quality multilingual datasets from web data.
Why it matters: We find that ensemble approaches improve precision but also substantially reduce coverage for low-resource languages.
What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
In this work we extend the OpenLID classifier by adding more training data, merging problematic language variant clusters, and introducing a special label for marking noise.
What's new
During development, we focus on three groups of closely related languages (Bosnian, Croatian, and Serbian; Romance varieties of Northern Italy and Southern France; and Scandinavian languages) and contribute new evaluation datasets where existing ones are in...
Key details
Existing LID tools (such as OpenLID or GlotLID) often struggle to identify closely related languages and to distinguish valid natural language from noise, which contaminates language-specific subsets, especially for low-resource languages.
In this work we extend the OpenLID classifier by adding more training data, merging problematic language variant clusters, and introducing a special label for marking noise.
We call this extended system OpenLID-v3 and evaluate it against GlotLID on multiple benchmarks.
During development, we focus on three groups of closely related languages (Bosnian, Croatian, and Serbian; Romance varieties of Northern Italy and Southern France; and Scandinavian languages) and contribute new evaluation datasets where existing ones are in...
Results & evidence
arXiv:2602.13139v4 Announce Type: replace Abstract: Language identification (LID) is an essential step in building high-quality multilingual datasets from web data.
Computer Science > Computation and Language [Submitted on 13 Feb 2026 (v1), last revised 18 Jun 2026 (this version, v4)] Title:OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report View PDF HTML (experimental...
Submission history From: Mariia Fedorova [view email][v1] Fri, 13 Feb 2026 17:47:08 UTC (63 KB) [v2] Mon, 23 Feb 2026 17:38:41 UTC (69 KB) [v3] Tue, 16 Jun 2026 11:13:12 UTC (70 KB) [v4] Thu, 18 Jun 2026 11:51:21 UTC (70 KB) References & Citations Loading...
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.
Reality Check
~1 min
affaan-m/ECC: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Primary source: yes
Demo available: no
Benchmarks/evals: no
Baselines/ablations: no
Third-party corroboration: no
Reproducibility details: yes
What would change my mind:
Independent replication with comparable or better results.
Public benchmark numbers with clear baseline comparisons.
Likely failure mode: Performance may collapse outside curated demos or narrow tasks.
paperclipai/paperclip: The open-source app everyone uses to manage agents at work
Primary source: yes
Demo available: no
Benchmarks/evals: no
Baselines/ablations: no
Third-party corroboration: no
Reproducibility details: yes
What would change my mind:
Independent replication with comparable or better results.
Public benchmark numbers with clear baseline comparisons.
Likely failure mode: Performance may collapse outside curated demos or narrow tasks.
Med-R2: Perception and Reflection-driven Complex Reasoning for Medical Report Generation
Primary source: yes
Demo available: yes
Benchmarks/evals: no
Baselines/ablations: no
Third-party corroboration: no
Reproducibility details: yes
What would change my mind:
Independent replication with comparable or better results.
Public benchmark numbers with clear baseline comparisons.
Likely failure mode: Performance may collapse outside curated demos or narrow tasks.
OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report
Primary source: yes
Demo available: no
Benchmarks/evals: yes
Baselines/ablations: no
Third-party corroboration: no
Reproducibility details: yes
What would change my mind:
Independent replication with comparable or better results.
Public benchmark numbers with clear baseline comparisons.
Likely failure mode: Performance may collapse outside curated demos or narrow tasks.
Lab Notes
~1 min
Tool/Repo of the day: affaan-m/ECC: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. (https://github.com/affaan-m/ECC)
Prompt/Workflow of the day: summarize claim -> evidence -> risk in three passes before acting.
Tiny snippet: `uv run python -m msd.run --scheduled`
Signal 9.4Novelty 4.0Impact 2.0Confidence 8.7Actionability 6.5
Summary: arXiv:2504.02885v2 Announce Type: replace Abstract: Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision.
What happened: We introduce a perception-driven long reasoning process that precedes report generation and incorporates radiology-specific knowledge as guidance.
Why it matters: Our experiments demonstrate that Med-R2 effectively enhances the capability of pathological features perception and diagnosis accuracy for MRG via fine-tuned LVLMs.
What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
arXiv:2504.02885v2 Announce Type: replace Abstract: Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision support.
What's new
Firstly, direct SFT enables LVLMs to generate medical reports directly without an intermediate thinking process of pathological feature perception and diagnostic reasoning.
Key details
Large vision-language models (LVLMs) hold great promise for automated MRG due to their fine-grained image-text alignment and advanced text-generation capabilities.
Currently, state-of-the-art MRGs primarily focus on adapting pre-trained LVLMs with direct supervised fine-tuning (SFT), a fine-tuning strategy with medical image-report pairs.
However, several factors limit the performance of these LVLMs.
Firstly, direct SFT enables LVLMs to generate medical reports directly without an intermediate thinking process of pathological feature perception and diagnostic reasoning.
Results & evidence
arXiv:2504.02885v2 Announce Type: replace Abstract: Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision support.
Computer Science > Computation and Language [Submitted on 2 Apr 2025 (v1), last revised 18 Jun 2026 (this version, v2)] Title:Med-R2: Perception and Reflection-driven Complex Reasoning for Medical Report Generation View PDF HTML (experimental)Abstract:Autom...
Submission history From: Hao Wang [view email][v1] Wed, 2 Apr 2025 08:18:54 UTC (1,248 KB) [v2] Thu, 18 Jun 2026 12:55:35 UTC (740 KB) References & Citations Loading...
Limitations / unknowns
However, several factors limit the performance of these LVLMs.
This causes a potential failure to perceive pathological features and thus leads to misdiagnosis.
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.
Signal 9.4Novelty 4.0Impact 2.0Confidence 8.7Actionability 6.5
Summary: arXiv:2602.13139v4 Announce Type: replace Abstract: Language identification (LID) is an essential step in building high-quality multilingual datasets from web data.
What happened: arXiv:2602.13139v4 Announce Type: replace Abstract: Language identification (LID) is an essential step in building high-quality multilingual datasets from web data.
Why it matters: We find that ensemble approaches improve precision but also substantially reduce coverage for low-resource languages.
What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
In this work we extend the OpenLID classifier by adding more training data, merging problematic language variant clusters, and introducing a special label for marking noise.
What's new
During development, we focus on three groups of closely related languages (Bosnian, Croatian, and Serbian; Romance varieties of Northern Italy and Southern France; and Scandinavian languages) and contribute new evaluation datasets where existing ones are in...
Key details
Existing LID tools (such as OpenLID or GlotLID) often struggle to identify closely related languages and to distinguish valid natural language from noise, which contaminates language-specific subsets, especially for low-resource languages.
In this work we extend the OpenLID classifier by adding more training data, merging problematic language variant clusters, and introducing a special label for marking noise.
We call this extended system OpenLID-v3 and evaluate it against GlotLID on multiple benchmarks.
During development, we focus on three groups of closely related languages (Bosnian, Croatian, and Serbian; Romance varieties of Northern Italy and Southern France; and Scandinavian languages) and contribute new evaluation datasets where existing ones are in...
Results & evidence
arXiv:2602.13139v4 Announce Type: replace Abstract: Language identification (LID) is an essential step in building high-quality multilingual datasets from web data.
Computer Science > Computation and Language [Submitted on 13 Feb 2026 (v1), last revised 18 Jun 2026 (this version, v4)] Title:OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report View PDF HTML (experimental...
Submission history From: Mariia Fedorova [view email][v1] Fri, 13 Feb 2026 17:47:08 UTC (63 KB) [v2] Mon, 23 Feb 2026 17:38:41 UTC (69 KB) [v3] Tue, 16 Jun 2026 11:13:12 UTC (70 KB) [v4] Thu, 18 Jun 2026 11:51:21 UTC (70 KB) References & Citations Loading...
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.
Signal 9.4Novelty 6.2Impact 2.0Confidence 8.3Actionability 5.2
Summary: arXiv:2606.20235v1 Announce Type: cross Abstract: Academic paper search is a core step in scientific research, and LLM-based search agents are emerging as a promising paradigm for.
What happened: arXiv:2606.20235v1 Announce Type: cross Abstract: Academic paper search is a core step in scientific research, and LLM-based search agents are emerging as a promising.
Why it matters: Benchmarking results show that agentic methods outperform single-shot retrieval baselines, yet the best-performing agent only achieves 0.314 Recall@100 and 0.355.
What to do: Track for corroboration and benchmark data before adopting.
Deep
Context
arXiv:2606.20235v1 Announce Type: cross Abstract: Academic paper search is a core step in scientific research, and LLM-based search agents are emerging as a promising paradigm for iterative, intent-driven literature exploration.
What's new
We propose ScholarQuest, a large-scale, taxonomy-guided benchmark for agentic academic paper search.
Key details
However, existing benchmarks are insufficient for systematically evaluating agentic academic search under realistic open literature environments.
We propose ScholarQuest, a large-scale, taxonomy-guided benchmark for agentic academic paper search.
ScholarQuest is constructed from over 1,000 computer science topics and four representative research intents, including method-oriented, setting-anchored, comparison-based, and scope-controlled queries.
It further provides scalable answer construction and a shared retrieval backend ScholarBase for reproducible evaluation.
Results & evidence
arXiv:2606.20235v1 Announce Type: cross Abstract: Academic paper search is a core step in scientific research, and LLM-based search agents are emerging as a promising paradigm for iterative, intent-driven literature exploration.
ScholarQuest is constructed from over 1,000 computer science topics and four representative research intents, including method-oriented, setting-anchored, comparison-based, and scope-controlled queries.
Benchmarking results show that agentic methods outperform single-shot retrieval baselines, yet the best-performing agent only achieves 0.314 Recall@100 and 0.355 Recall@All, indicating substantial room for improvement.
Limitations / unknowns
However, existing benchmarks are insufficient for systematically evaluating agentic academic search under realistic open literature environments.
In addition, analyses of search efficiency, intent-level robustness, and failure cases further highlight the benchmark's ability to provide multi-dimensional evaluation signals for academic paper search agents.
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.
Signal 10.0Novelty 5.1Impact 8.2Confidence 7.0Actionability 6.5
Summary: An agent-managed museum exhibit, built in Rust with Gajae-Code / LazyCodex — developed and maintained with no human intervention.
What happened: An agent-managed museum exhibit, built in Rust with Gajae-Code / LazyCodex — developed and maintained with no human intervention.
Why it matters: An agent-managed museum exhibit, built in Rust with Gajae-Code / LazyCodex — developed and maintained with no human intervention.
What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
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.
Signal 10.0Novelty 5.1Impact 7.8Confidence 7.0Actionability 6.5
Summary: A collection of DESIGN.md files analysis by popular brand design systems.
What happened: DESIGN.md is a new concept introduced by Google Stitch.
Why it matters: A collection of DESIGN.md files analysis by popular brand design systems.
What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
A collection of DESIGN.md files analysis by popular brand design systems.
What's new
DESIGN.md is a new concept introduced by Google Stitch.
Key details
Drop one into your project and let coding agents generate a matching UI.
Copy a DESIGN.md into your project, tell your AI agent “build me a page that looks like this,” and generate high-quality UI that stays visually consistent with the design language.
Built with real design depth — including analyzed patterns, tokens, and rules — for high-quality UI generation, not surface-level outputs.
DESIGN.md is a new concept introduced by Google Stitch.
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.
Signal 9.4Novelty 5.1Impact 2.0Confidence 8.3Actionability 5.2
Summary: arXiv:2606.19887v1 Announce Type: cross Abstract: Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks.
What happened: We introduce FinRED, an expert-guided red-teaming framework for financial LLM safety evaluation developed with financial experts.
Why it matters: arXiv:2606.19887v1 Announce Type: cross Abstract: Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks.
What to do: Track for corroboration and benchmark data before adopting.
Deep
Context
FinRED uses a novel two-level taxonomy mapping global standards (e.g., FATF and EU DORA) to threats ranging from regulatory evasion to complex fraud, integrated with a scalable pipeline that converts real financial documents into context-rich red-teaming Be...
What's new
arXiv:2606.19887v1 Announce Type: cross Abstract: Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks.
Key details
Financial LLMs face regulatory compliance violations, fraud facilitation, and systemic trust erosion that require targeted evaluation.
We introduce FinRED, an expert-guided red-teaming framework for financial LLM safety evaluation developed with financial experts.
FinRED uses a novel two-level taxonomy mapping global standards (e.g., FATF and EU DORA) to threats ranging from regulatory evasion to complex fraud, integrated with a scalable pipeline that converts real financial documents into context-rich red-teaming Be...
Rigorous expert validation confirms seed plausibility and realism for meaningful LLM safety evaluation.
Results & evidence
arXiv:2606.19887v1 Announce Type: cross Abstract: Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks.
We also provide an expert-validated, finance-specific rubric that goes beyond disclaimer checks, aligns more closely with human experts than static one-size-fits-all rubrics, and reduces critical false negatives from 28 to 12.
Aligned with internationally adopted risk-management and information-security standards (e.g., ISO/IEC 27001), FinRED is deployed in South Korea's Financial Security Institute (FSI) regulatory sandbox for generative AI security evaluation in real financial...
Limitations / unknowns
arXiv:2606.19887v1 Announce Type: cross Abstract: Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks.
Aligned with internationally adopted risk-management and information-security standards (e.g., ISO/IEC 27001), FinRED is deployed in South Korea's Financial Security Institute (FSI) regulatory sandbox for generative AI security evaluation in real financial...
To mitigate dual-use risks, the dataset, generation pipeline, prompt template, and evaluation framework are gated for qualified researchers at https://github.com/selectstar-ai/FinRED-paper and https://huggingface.co/datasets/datumo/FinRED.
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.