[upstream-sync] README.md from VoltAgent/awesome-ai-agent-papers@29037d57 [catalogue]

Reviewed-on: #34

upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/.github/workflows/link-check.yml

@@ -2,9 +2,9 @@
 title: "Link Check"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/539ec1dc/.github/workflows/link-check.yml
-upstream_sha: 539ec1dc
-imported_at: 2026-06-26
+upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/117f624b/.github/workflows/link-check.yml
+upstream_sha: 117f624b
+imported_at: 2026-07-25
 prompt_class: unknown
 upstream_changes: accepted
 author: upstream
@@ -40,7 +40,7 @@ jobs:
           args: >-
             --verbose
             --no-progress
-            --accept 200,204,301,302,403,429
+            --accept 200,204,301,302,308,403,429
             --exclude-path node_modules
             --exclude "star-history.com"
             --exclude "shields.io"

upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/README.md

@@ -2,9 +2,9 @@
 title: "Awesome AI Agents 2026"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/a00bf4c7/README.md
-upstream_sha: a00bf4c7
-imported_at: 2026-07-08
+upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/117f624b/README.md
+upstream_sha: 117f624b
+imported_at: 2026-07-25
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -68,6 +68,8 @@ validated: false
 - [Changelog](#changelog)
 - [Star History](#star-history)
 
+![agent](img/agent.png) ![loop](img/loop.png)
+
 ## Orchestration Frameworks
 
 - [Agency Swarm](https://github.com/VRSEN/agency-swarm) `🚀` `[Python]` `[Multi-Agent]` - Orchestrates multi-agent systems built on the OpenAI Assistants API with role-based collaboration.
@@ -114,12 +116,14 @@ validated: false
 - [Cursor](https://cursor.com) `🚀` `[TypeScript]` `[IDE]` - AI-native IDE with parallel Cloud Agents and 100K+ enterprise users.
 - [Devika](https://github.com/stitionai/devika) `🌱` `[Python]` `[Docker]` - Open-source AI software engineer that understands high-level instructions and writes full-featured code autonomously.
 - [Devin](https://devin.ai) `🚀` `[Cloud]` `[GitHub]` - Fully autonomous AI software engineer showing 8-12x efficiency gains in production (Nubank case study), cloud sandbox with persistent state, GitHub integration, and enterprise adoption growing 40% MoM.
+- [fractal](https://github.com/plasma-ai/fractal) `🌱` `[Python]` `[Multi-Agent]` - Orchestrates Claude Code, Codex, and other agents in per-node Git worktrees with recursive delegation and a live TUI.
 - [gemini-cli](https://github.com/google-gemini/gemini-cli) `🌱` `[Go]` `[CLI]` - Open-source CLI agent bringing Gemini AI directly into your terminal.
 - [GitHub Copilot](https://github.com/features/copilot) `🌱` `[TypeScript]` `[VS Code]` - Agent Mode in VS Code with Copilot Workspace for issue-to-PR workflows across Claude, GPT, and Gemini.
 - [Goose](https://github.com/aaif-goose/goose) `🌱` `[Rust]` `[CLI]` - Extensible open-source CLI agent that installs, executes, edits, and tests with any LLM.
 - [JetBrains AI](https://www.jetbrains.com/ai/) `🌱` `[Kotlin]` `[JetBrains]` - Deep AI integration across all JetBrains IDEs with context-aware completions and refactoring.
+- [Juggler](https://github.com/juggler-ai/juggler) `🌱` `[Desktop]` `[Local]` - Multi-client desktop/remote GUI agent with inspectable tool calls, branching-thread editable context, and plugin extensibility.
 - [Kiro](https://kiro.dev) `🚀` `[Cloud]` `[IDE]` - Spec-driven development agent that writes specs, auto-generates tasks, implements code, and automates DevOps workflows.
-- [Open Interpreter](https://github.com/openinterpreter/open-interpreter) `🌱` `[Python]` `[CLI]` - Execute code locally via natural-language model instructions with a ChatGPT-like interface.
+- [Open Interpreter](https://github.com/openinterpreter/openinterpreter) `🌱` `[Python]` `[CLI]` - Execute code locally via natural-language model instructions with a ChatGPT-like interface.
 - [opencode](https://github.com/anomalyco/opencode) `🌱` `[TypeScript]` `[Desktop]` - Open-source coding agent available as a desktop app with a visual interface.
 - [OpenHands](https://github.com/OpenHands/OpenHands) `🌱` `[Python]` `[Docker]` - AI-driven development platform that writes, tests, and deploys code autonomously.
 - [PR-Agent](https://github.com/The-PR-Agent/pr-agent) `🚀` `[Python]` `[GitHub]` - Open-source AI PR reviewer that auto-describes, reviews, and improves pull requests.
@@ -139,12 +143,13 @@ validated: false
 - [Chroma](https://github.com/chroma-core/chroma) `🌱` `[Python]` `[Vector DB]` - Lightweight, embeddable vector store for building memory-augmented AI agents with fast semantic retrieval.
 - [cognee](https://github.com/topoteretes/cognee) `🌱` `[Python]` `[Graph-Based]` - Knowledge engine for AI agent memory, set up in 6 lines of code with graph-based knowledge extraction.
 - [Cortex Memory](https://github.com/prem-research/cortex) `🌱` `[Python]` `[Vector DB]` - Full-stack solution for agent memory covering extraction, vector search, and optimization.
+- [Engrava](https://github.com/sovantica/engrava) `🔬` `[Python]` `[Graph-Based]` - Stores agent memory as a typed knowledge graph with hybrid search and a tamper-evident journal, embedded in SQLite.
 - [graphiti](https://github.com/getzep/graphiti) `🌱` `[Python]` `[Multi-Agent]` - Build real-time knowledge graphs for AI agents with automatic entity extraction and linking.
 - [Kage](https://github.com/kage-core/Kage) `🌱` `[TypeScript]` `[MCP]` - Git-native memory for coding agents that stores decisions and fixes as repo files and verifies them against the codebase, withholding stale knowledge.
 - [LanceDB](https://github.com/lancedb/lancedb) `🌱` `[Rust]` `[Vector DB]` - Serverless vector search database embedded directly in the agent process with no infrastructure needed.
 - [Langmem](https://github.com/langchain-ai/langmem) `🌱` `[Python]` `[LangChain]` - Helps agents learn and adapt from their interactions over time with persistent memory.
 - [Mem0](https://github.com/mem0ai/mem0) `🌱` `[Python]` `[Vector DB]` - Memory layer for AI applications with long-term, short-term, and semantic memory extraction.
-- [Remio](https://remio.ai/) `🔬` `[Desktop]` `[Memory]` - Local-first AI memory desktop app that parses files into searchable vector indexes.
+- [Remio](https://www.remio.ai/) `🔬` `[Desktop]` `[Memory]` - Local-first AI memory desktop app that parses files into searchable vector indexes.
 - [Memoir](https://github.com/zhangfengcdt/memoir) `🔬` `[Python]` `[Memory]` - Git-like versioned semantic memory for AI agents with branching and commits.
 - [Memvid](https://github.com/memvid/memvid) `🌱` `[Python]` `[RAG]` - Replace complex RAG pipelines with a serverless, single-file memory layer for instant retrieval.
 - [Milvus](https://github.com/milvus-io/milvus) `🌱` `[Go]` `[Vector DB]` - Scales vector search to billions of embeddings for large-scale agent knowledge bases.
@@ -199,7 +204,7 @@ The protocol layer that enables agents to discover tools, communicate with each
 - [mcp-nest](https://github.com/CharanBharathula/mcp-nest) `🌱` `[Python]` `[MCP]` - Unified Model Context Protocol (MCP) server for executing code and managing files.
 - [NotFair](https://notfair.co) `🚀` `[Cloud]` `[MCP]` - Hosted Google Ads MCP server for diagnosing, optimizing, and executing campaign changes via the Google Ads API with a human-approval gate.
 - [NotFair Skills](https://github.com/nowork-studio/NotFair) `🚀` `[TypeScript]` `[MCP]` - Open-source Claude Code skills for SEO, GEO, Google Ads, and Meta Ads, connecting to live data through Google Ads MCP, Meta Ads MCP, Google Search Console MCP, and Google Analytics (GA4) MCP.
-- [Toolhouse](https://toolhouse.ai) `🌱` `[Python]` `[Multi-Agent]` - Cloud-hosted tool infrastructure for agents with optimized execution and low-latency access.
+- [Toolhouse](https://toolhouse.ai/en/) `🌱` `[Python]` `[Multi-Agent]` - Cloud-hosted tool infrastructure for agents with optimized execution and low-latency access.
 - [Zapier MCP Server](https://zapier.com/mcp) `🌱` `[Cloud]` `[MCP]` - Connect agents to 7,000+ app integrations via MCP, powered by Zapier's automation platform.
 - [zero-api-key-web-search](https://github.com/wd041216-bit/zero-api-key-web-search) `🌱` `[Python]` `[MCP]` - Free web search toolkit for AI agents with no API keys, MCP server support.
 
@@ -227,8 +232,10 @@ The protocol layer that enables agents to discover tools, communicate with each
 
 Sandboxes, web scrapers, browser automation, and networking layers that agents depend on.
 
+- [Agent Bounties](https://github.com/NSPG13/agent-bounties) `🔬` `[Rust]` `[MCP]` - Coordinates verifiable digital bounty workflows designed for agents to post, fund, claim, solve, verify, and earn.
 - [AgentDock](https://github.com/agentdock/agentdock) `🚀` `[Python]` `[Docker]` - Framework for building and deploying production-ready AI agents with composable node architecture.
-- [Agent Starter](https://github.com/raintree-technology/agent-starter) `🌱` `[TypeScript]` `[MCP]` - Project-local config manager that syncs one `agent.json` manifest into Claude Code, Codex, Cursor, and MCP setup while preserving manual edits and detecting drift.
+- [AgentServices](https://agentservices.to) `🚀` `[Python]` `[x402]` - Paid data APIs for AI agents with 54 services, 37 MCP tools, and x402 nanopayments on Base. Market data, onchain analytics, AI inference, and research.
+- [AgentWork](https://agentwork-api.yfoob.chatgpt.site/) `🔬` `[Cloud]` `[x402]` - Aggregates verified paid work opportunities for autonomous agents, with a 0.005 Polygon USDC/hour x402 API for full decision context.
 - [codex-profiles](https://github.com/Ducksss/codex-profiles) `🚀` `[Python]` `[OpenAI]` - Bash CLI for switching OpenAI Codex CLI and Desktop profiles with isolated CODEX_HOME directories.
 - [Crawl4AI](https://github.com/unclecode/crawl4ai) `🌱` `[Python]` `[Multi-Agent]` - Extracts structured data from web pages using LLM-friendly output formats optimized for agent ingestion.
 - [Docling](https://github.com/docling-project/docling) `🌱` `[Python]` `[IDE]` - Parses PDFs, DOCX, and slides into structured text with deep layout understanding for document agents.
@@ -239,7 +246,7 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
 - [LlamaParse](https://github.com/run-llama/llama_cloud_services) `🌱` `[Python]` `[RAG]` - GenAI-native document parser designed to extract complex tables and layouts for RAG pipelines.
 - [Marker](https://github.com/datalab-to/marker) `🌱` `[Python]` `[CLI]` - Converts PDF documents to markdown with high accuracy for tables, equations, and figures.
 - [Notte](https://github.com/nottelabs/notte) `🚀` `[Python]` `[Pipeline]` - Browser automation engine optimized for production AI pipelines.
-- [Pilot Protocol](https://github.com/TeoSlayer/pilotprotocol) `🌱` `[Go]` `[Multi-Agent]` - Networking stack for distributed agent systems with encrypted tunnels.
+- [Pilot Protocol](https://github.com/pilot-protocol/pilotprotocol) `🌱` `[Go]` `[Multi-Agent]` - Networking stack for distributed agent systems with encrypted tunnels.
 - [Playwright](https://github.com/microsoft/playwright) `🌱` `[TypeScript]` `[Testing]` - Automates Chromium, Firefox, and WebKit browsers with a single cross-language API for agent-driven testing.
 - [ScrapeGraphAI](https://github.com/ScrapeGraphAI/Scrapegraph-ai) `🌱` `[Python]` `[LangChain]` - Python web-scraping library that uses LLMs to build intelligent scraping pipelines from natural-language instructions.
 - [Surya](https://github.com/datalab-to/surya) `🌱` `[Python]` `[CLI]` - Runs OCR and layout detection on documents in 90+ languages for multilingual document agents.
@@ -256,6 +263,8 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
 - [Flowise](https://github.com/FlowiseAI/Flowise) `🌱` `[TypeScript]` `[RAG]` - Open-source drag-and-drop UI to build customized LLM flows, RAG pipelines, and agent systems.
 - [Langflow](https://github.com/langflow-ai/langflow) `🌱` `[Python]` `[RAG]` - Visual drag-and-drop builder for LLM workflows, RAG agents, and multi-step pipelines.
 - [Lindy](https://www.lindy.ai) `🌱` `[Cloud]` `[No-Code]` - No-code AI agent platform with 3000+ app integrations for business workflow automation.
+- [LlamaIndex Workflows](https://docs.llamaindex.ai/en/stable/module_guides/workflow/) `🌱` `[Python]` `[RAG]` - Event-driven orchestration framework for building complex agentic systems.
+- [LLMGraph](https://llmgraph.ai/home) `🔬` `[Cloud]` `[RAG]` - Visual canvas for building RAG chatbots and AI agents with one-click deploy to REST API or chat widget.
 - [Make](https://www.make.com/en) `🌱` `[Cloud]` `[RAG]` - Visual workflow automation platform with AI capabilities and drag-and-drop scenario builder.
 - [n8n](https://github.com/n8n-io/n8n) `🌱` `[TypeScript]` `[Docker]` - Open-source workflow automation with AI agent nodes combining visual and code-based orchestration.
 - [Relevance AI](https://relevanceai.com) `🌱` `[Cloud]` `[No-Code]` - No-code AI agent builder for sales, support, and research use cases with team collaboration.
@@ -289,6 +298,7 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
 - [Braintrust](https://www.braintrust.dev) `🌱` `[TypeScript]` `[Evaluation]` - Eval-driven development platform with experiment tracking and prompt optimization for agent quality.
 - [ElevenAgents](https://elevenlabs.io/agents) `🚀` `[Cloud]` `[Voice]` - Voice agent platform from ElevenLabs for customer support automation with HIPAA compliance and multi-language support.
 - [DriftGuard](https://github.com/sujal-maheshwari2004/DriftGuard) `🌱` `[Python]` `[Multi-Agent]` - Semantic memory guardrails using causal graphs to prevent agents from repeating past failures.
+- [Galley](https://github.com/shinpr/galley) `🔬` `[Go]` `[Multi-Agent]` - Pairs independently configured executors and supervisors with repository-defined quality gates and inspectable evidence for each coding attempt.
 - [Guardrails AI](https://github.com/guardrails-ai/guardrails) `🌱` `[Python]` `[Multi-Agent]` - Adds structural, type, and quality guarantees to LLM outputs for reliable agent responses.
 - [Helicone](https://github.com/Helicone/helicone) `🌱` `[TypeScript]` `[Observability]` - Open-source LLM observability with one-line integration for cost tracking and prompt analytics.
 - [Lakera Guard](https://www.lakera.ai) `🌱` `[Cloud]` `[Security]` - Real-time protection against prompt injection, data leakage, and toxicity in agent interactions.
@@ -297,10 +307,15 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
 - [LangSmith](https://smith.langchain.com) `🚀` `[Cloud]` `[LangChain]` - LangChain platform for tracing, testing, and evaluating agent performance with production monitoring.
 - [LLM Guard](https://github.com/protectai/llm-guard) `🌱` `[Python]` `[Security]` - Security toolkit for scanning LLM inputs and outputs to prevent prompt injection and data leaks.
 - [Logfire](https://github.com/pydantic/logfire) `🌱` `[Python]` `[Pydantic]` - Python-native observability from the Pydantic team with deep integration for high-performance agent monitoring.
+- [MartinLoop](https://github.com/Keesan12/martin-loop) `🔬` `[TypeScript]` `[Compliance]` - Governs autonomous coding agents, turning open-ended runs into budgeted, verified software work with signed outcome receipts.
 - [NeMo Guardrails](https://github.com/NVIDIA-NeMo/Guardrails) `🌱` `[Python]` `[Multi-Agent]` - NVIDIA programmable guardrails toolkit for controlling and securing LLM-powered agent conversations.
 - [Orchard Kit](https://github.com/OrchardHarmonics/orchard-kit) `🌱` `[Python]` `[Security]` - Modules for agent runtime security, self-audit trails, and collective cognition patterns.
 - [OWASP Top 10 for Agentic Apps](https://owasp.org/www-project-top-10-for-large-language-model-applications/) `🌱` `[Python]` `[Security]` - Security framework covering goal hijacking, tool misuse, and cascading failure mitigations for agents.
+- [Pluribus](https://github.com/caioribeiroclw-pixel/pluribus) `🔬` `[TypeScript]` `[Observability]` - Generates cross-tool agent context and privacy-safe evidence receipts for loaded authority, handoffs, and skill use.
 - [Rebuff](https://github.com/protectai/rebuff) `🌱` `[Python]` `[Security]` - Self-hardening prompt injection detection system for securing agent inputs against adversarial attacks.
+- [Agent Learning Kit](https://github.com/future-agi/agent-learning-kit) `🌱` `[Python]` `[Evaluation]` - LLM evaluation framework with 50+ metrics, LLM-as-Judge, and guardrail scanners (jailbreak, PII, injection).
+- [Shipmoor](https://shipmoor.dev) `🔬` `[Python]` `[Testing]` - Local, deterministic verification layer for AI agent code: scans, test evidence, and a binding merge verdict without uploading source.
+- [SourceryKit](https://github.com/ProvablyAI/sourcerykit) `🔬` `[Python]` `[Security]` - Verifies an agent's outbound requests and MCP handoffs against a source of truth using zero-knowledge proofs, logging each call and blocking anything off the trusted-endpoint allow-list.
 - [ai-evaluation](https://github.com/future-agi/ai-evaluation) `🌱` `[Python]` `[Evaluation]` - LLM evaluation framework with 50+ metrics, LLM-as-Judge, and guardrail scanners (jailbreak, PII, injection).
 - [Future AGI](https://github.com/future-agi/future-agi) `🌱` `[Python]` `[Self-Hosted]` - Self-hostable end-to-end agent engineering platform with tracing, evals, guardrails, and gateway.
 
@@ -310,12 +325,15 @@ Frontend workspaces and chat interfaces with built-in agent plugins and tool-use
 
 - [AionUi](https://github.com/iOfficeAI/AionUi) `🚀` `[Desktop]` `[Multi-Agent]` - Connects 20+ AI CLIs and any API key in a local, open-source multi-agent desktop workspace.
 - [AnythingLLM](https://github.com/Mintplex-Labs/anything-llm) `🌱` `[TypeScript]` `[RAG]` - All-in-one AI application with RAG, agents, and multi-model support for desktop and Docker.
+- [Better Agent](https://github.com/ofekron/better-agent) `🔬` `[Desktop]` `[Multi-Agent]` - Manages Claude, Codex, and Gemini coding-agent sessions with parallel forks, delegation, persistence, and restart recovery.
 - [DB-GPT](https://github.com/eosphoros-ai/DB-GPT) `🌱` `[Python]` `[Database]` - Data interaction platform with local LLM support for 100% private database and analytics agents.
 - [LibreChat](https://github.com/danny-avila/LibreChat) `🌱` `[TypeScript]` `[IDE]` - Self-hosted multi-model chat interface supporting all major AI providers with access control.
 - [LobeHub](https://lobehub.com/) `🌱` `[TypeScript]` `[Multi-Agent]` - Modern platform for hybrid work and AI-driven collaboration with extensible agent teams and rapid integration.
 - [LobeChat](https://github.com/lobehub/lobehub) `🌱` `[TypeScript]` `[Multi-Agent]` - Modern, open-source AI chat framework with a massive plugin ecosystem for autonomous agent capabilities.
 - [OpenHuman](https://github.com/tinyhumansai/openhuman) `🚀` `[Rust]` `[Memory]` - Self-hosted local-first personal AI assistant with a Rust core, desktop apps, knowledge-graph memory, skills, voice, and multi-channel messaging.
+- [Orkas](https://github.com/Orkas-AI/Orkas) `🔬` `[Desktop]` `[Multi-Agent]` - Runs parallel AI agents in a local-first desktop workspace with shared files, BYOK providers, and optional sync.
 - [OpenWebUI](https://github.com/open-webui/open-webui) `🌱` `[TypeScript]` `[RAG]` - Extensible local AI interface with built-in RAG, tool use, and support for multi-agent workflows.
+- [lucinate](https://github.com/lucinate-ai/lucinate) `🌱` `[Go]` `[TUI]` - Multi-backend terminal AI chat client for OpenClaw, Hermes, Ollama, and OpenAI-compatible APIs with routines, multi-agent switching, and local agent skills.
 
 ## Agent Deployment and Hosting
 
@@ -365,6 +383,8 @@ Curated list of vertical agent solutions for finance, healthcare, legal, manufac
 - [JPMorgan AlphaBlue](https://www.jpmorgan.com/global) `🚀` `[Cloud]` `[Multi-Agent]` - Trading and market analysis agent integrations for institutional workflows.
 - [Goldman Sachs Marquee AI](https://www.goldmansachs.com) `🚀` `[Cloud]` `[Multi-Agent]` - Market data and analytics agents built on Marquee platform.
 - [Morgan Stanley AdvisorBot](https://www.morganstanley.com) `🚀` `[Cloud]` `[CLI]` - Financial advisory assistant for advisors and retail clients.
+- [AgentPump](https://agentpump.app) `🔬` `[Cloud]` `[CLI]` - Runs autonomous on-chain memecoin trading agents on Solana that trade on a schedule, operable from the terminal via the @agentpump/cli.
+
 
 ### Healthcare
 
@@ -386,6 +406,7 @@ Curated list of vertical agent solutions for finance, healthcare, legal, manufac
 ### Government & Compliance
 
 - Anthropic Government Agents - Policy analysis and public sector agents for regulated workflows (🏷️ `Cloud` `Government` `Enterprise`).
+- [Leyna](https://www.leyna.ai) `🌱` `[Cloud]` `[Government]` - Public records request agent for government agencies to parse, redact, and track requests.
 - Tax & Insurance Agent Platforms - Generic category placeholder for compliance-focused tax and underwriting agents (🏷️ `Cloud` `Compliance` `Enterprise`).
 
 ## Learning Resources
@@ -397,6 +418,7 @@ Curated list of vertical agent solutions for finance, healthcare, legal, manufac
 - [Building Effective Agents](https://www.anthropic.com/engineering/building-effective-agents) `🚀` `[Python]` `[Anthropic]` - Anthropic's guide on agent design patterns, evaluation strategies, and production best practices.
 - [Hugging Face Agents Course](https://huggingface.co/learn/agents-course/unit0/introduction) `🚀` `[Python]` `[Multi-Agent]` - Open-source course on building AI agents using Hugging Face tools and models.
 - [Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations](https://arxiv.org/abs/2509.16457) `🌱` `[Python]` `[Benchmark]` - EMNLP 2025 paper introducing PersonaEvolve, an LLM-based optimizer that refines agent personas so crowds of LLM agents behave realistically against expert benchmarks.
+- [Jailbreaking LLMs' Safeguard with Universal Magic Words for Text Embedding Models](https://arxiv.org/abs/2501.18280) `🔬` `[Python]` `[Paper]` - Universal suffix that manipulates text-embedding similarity to bypass safety guardrails across ChatGPT, DeepSeek, and Qwen.
 - [LATS: Language Agent Tree Search](https://github.com/lapisrocks/LanguageAgentTreeSearch) `🌱` `[Python]` `[Paper]` - Combines Monte Carlo tree search with LLM reasoning for complex multi-step planning tasks.
 - [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) `🌱` `[Python]` `[Multi-Agent]` - Deep breakdown of LLM-powered agent components: planning, memory, and tool use.
 - [Microsoft GenAI for Beginners](https://github.com/microsoft/generative-ai-for-beginners) `🚀` `[Python]` `[Microsoft]` - A 21-lesson course on generative AI concepts and agent development from Microsoft.
@@ -405,6 +427,7 @@ Curated list of vertical agent solutions for finance, healthcare, legal, manufac
 - [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) `🌱` `[Python]` `[Paper]` - The foundational paper behind the ReAct prompting pattern used in most agent frameworks.
 - [Reflexion](https://github.com/noahshinn/reflexion) `🌱` `[Python]` `[Paper]` - Research framework letting agents learn from past mistakes via iterative verbal self-reflection loops.
 - [Tree of Thoughts](https://github.com/princeton-nlp/tree-of-thought-llm) `🌱` `[Python]` `[Paper]` - Explores multiple parallel reasoning paths before committing to a final answer for complex problems.
+- [What's Missing in Autonomous Research?](https://haizhaoyang.github.io/research/autoresearch-survey.html) `🔬` `[Python]` `[Research]` - Systematizes 56 autonomous research systems across seven axes, showing most can generate but few can defend research artifacts.
 
 ## Agent Communication
 
@@ -431,6 +454,7 @@ Frameworks for orchestrating data transformations and long-running agent-driven
 - [Hamilton](https://github.com/apache/hamilton) `🌱` `[Python]` `[Pipeline]` - Defines modular data transformations as Python functions wired automatically into a DAG for agent pipelines.
 - [Hex AI](https://hex.tech) `🌱` `[Cloud]` `[Multi-Agent]` - Collaborative data platform with AI-powered analysis and notebook-based data exploration for teams.
 - [Julius AI](https://julius.ai) `🌱` `[Cloud]` `[Multi-Agent]` - Upload CSV or Excel files and analyze data using natural language questions for instant insights.
+- [Nika](https://github.com/supernovae-st/nika) `🌱` `[Rust]` `[Workflow]` - Runs repeated AI work as reviewable YAML DAGs, statically checked for schema, permits, and an honest cost floor before any token is spent, with tamper-evident traces.
 - [PandasAI](https://github.com/sinaptik-ai/pandas-ai) `🌱` `[Python]` `[Multi-Agent]` - Chat with your data using natural language queries that convert to Pandas and SQL operations.
 - [Prefect](https://github.com/PrefectHQ/prefect) `🌱` `[Python]` `[Pipeline]` - Orchestrates agent workflows and data pipelines with retries, caching, and built-in observability.
 - [Signals CLI](https://signals.dev) `🌱` `[Cloud]` `[CLI]` - Intent signal CLI detecting LinkedIn engagers, keyword posters, and funding events with JSON output for agent pipelines.
@@ -513,7 +537,7 @@ Tools for generating images, video, music, audio, and 3D assets using AI models.
 - [LTX Video](https://github.com/Lightricks/LTX-Video) `🌱` `[Python]` `[IDE]` - Commercially safe open-source video generation model trained on fully licensed data.
 - [Luma Dream Machine](https://lumalabs.ai/app) `🌱` `[Cloud]` `[IDE]` - Creates physics-accurate 4K HDR video scenes with 3D-style output for cinematic quality.
 - [Pika 2.5](https://pika.art/login) `🌱` `[Cloud]` `[IDE]` - Beginner-friendly video generation tool with fast turnaround and intuitive editing controls.
-- [Runway Gen-4.5](https://runwayml.com) `🌱` `[Cloud]` `[IDE]` - Professional video generation with Motion Brush and the highest benchmark scores for visual quality.
+- [Runway Gen-4.5](https://runway.com) `🌱` `[Cloud]` `[IDE]` - Professional video generation with Motion Brush and the highest benchmark scores for visual quality.
 - [Seedance 2.0](https://seed.bytedance.com/en/seedance) `🌱` `[Cloud]` `[IDE]` - Quad-modal input video generator with built-in lip-sync and 2K resolution output.
 - [Sora 2](https://sora.com) `🚀` `[Cloud]` `[OpenAI]` - Generates 25-second cinematic video clips with strong narrative coherence from text prompts by OpenAI.
 - [Wan 2.1](https://github.com/Wan-Video/Wan2.1) `🌱` `[Python]` `[IDE]` - Open-source self-hosted video generation model with unlimited length and no per-video costs.
@@ -549,6 +573,7 @@ AI agents that automate customer support, CRM workflows, sales outreach, and tic
 - [Assembled](https://www.assembled.com) `🚀` `[Cloud]` `[Multi-Agent]` - Routes support tickets with workforce-aware scheduling and intelligent handoff to human agents.
 - [Dixa](https://www.dixa.com) `🚀` `[Cloud]` `[Multi-Agent]` - CRM-first conversational support platform with AI-powered routing and customer context enrichment.
 - [Freshdesk Freddy AI](https://www.freshworks.com/freshdesk/omni/freddy-ai-automation/) `🌱` `[Cloud]` `[Multi-Agent]` - Auto-triages and routes support tickets with smart AI suggestions for budget-conscious SMB teams.
+- [Hellomatik](https://hellomatik.com) `🌱` `[Cloud]` `[Multi-Agent]` - Turns company knowledge into agents that answer, book, and sell across WhatsApp, phone, email, and web.
 - [Intercom Fin](https://fin.ai) `🚀` `[Cloud]` `[Multi-Agent]` - Resolves 50% of SaaS support tickets by learning directly from your help center and knowledge base.
 - [Zendesk AI](https://www.zendesk.com/service/ai/) `🚀` `[Cloud]` `[Multi-Agent]` - Automates 30% of enterprise support tickets with deep integration into the existing Zendesk ecosystem.
 
@@ -571,7 +596,8 @@ AI agents that automate customer support, CRM workflows, sales outreach, and tic
 - [BuyWhere](https://buywhere.ai) `🌱` `[Cloud]` `[MCP]` - AI shopping agent for real-time product search and price comparison across SG/SEA/US markets using an MCP-native server with 8 tools.
 - [Clay](https://www.clay.com) `🌱` `[Cloud]` `[IDE]` - Enriches leads from 70+ data providers and generates hyper-personalized outreach at scale.
 - [Instantly](https://instantly.ai) `🌱` `[Cloud]` `[Multi-Agent]` - Generates AI cold emails with smart sender rotation and built-in domain warmup for deliverability.
-- [Lavender](https://www.lavender.ai) `🌱` `[Cloud]` `[Multi-Agent]` - Coaches email writing in real-time with AI response scoring and recipient intelligence.
+- [Lavender](https://lavender.ai/) `🌱` `[Cloud]` `[Multi-Agent]` - Coaches email writing in real-time with AI response scoring and recipient intelligence.
+- [OutreachAgent](https://outreachagent.dev/for-agents) `🔬` `[Cloud]` `[Event-Driven]` - Runs reply-aware outbound email workflows with webhooks, sender pacing, approvals, and deliverability guardrails.
 - [Overloop CLI](https://overloop.com) `🌱` `[Cloud]` `[CLI]` - AI outbound CLI agent that sources 450M+ contacts and runs email plus LinkedIn campaigns with JSON output.
 
 ## Voice Agent Platforms
@@ -608,6 +634,7 @@ AI platforms that conduct autonomous multi-step research, synthesize findings fr
 | Gemini Research  | 1M tokens      | Google Search + KG          |
 | Perplexity Pro   | Variable       | Real-time cited search      |
 
+- [Agon](https://github.com/AutoResearch-Factory/Agon) `🔬` `[Python]` `[Multi-Agent]` - Omnidisciplinary autonomous research system that replaces one-off prompts with Prompt Economy's reusable loops.
 - [CAJAL](https://github.com/Agnuxo1/CAJAL) `🌱` `[Python]` `[Local]` - Local AI agent that generates publication-ready IMRaD scientific papers with verified arXiv citations and AI tribunal scoring.
 - [ChatGPT Deep Research](https://openai.com/index/introducing-deep-research) `🚀` `[Cloud]` `[OpenAI]` - Conducts extended reasoning with web browsing to produce structured research reports with Canvas output.
 - [Claude Deep Research](https://www.anthropic.com/research) `🚀` `[Cloud]` `[Anthropic]` - Performs multi-step investigation with verified source citations and 200K token context window.
@@ -635,6 +662,7 @@ Platforms that generate full-stack applications, components, or web apps from na
 - [PlayCode Agent](https://playcode.io) `🌱` `[Cloud]` `[Multi-Agent]` - Browser-only lightweight web app creation tool with AI-assisted code generation.
 - [Replit Agent](https://replit.com/agent4) `🌱` `[Cloud]` `[Multi-Agent]` - Builds and deploys full-stack projects from prompts with integrated hosting and collaboration.
 - [v0 by Vercel](https://v0.app) `🚀` `[Cloud]` `[CLI]` - Generates React and Tailwind UI components from text descriptions with one-click Vercel deployment.
+- [VibeKit.bot](https://vibekit.bot) `🔬` `[Cloud]` `[Stateful]` - Builds and hosts full-stack apps from phone chat, with a persistent per-app agent and BYOK keys.
 
 ## Multi-Agent Consumer Platforms
 
@@ -743,6 +771,7 @@ Key data points on the AI agent market covering adoption, growth, valuations, an
 
 Tools for running LLMs locally and self-hosting AI agent platforms with full privacy and control.
 
+- [Avelina](https://avelina.ai) `🌱` `[TypeScript]` `[Telegram]` - Self-hosted personal AI assistant on your own VPS that lives in Telegram, with persistent long-term memory, voice, and Claude-powered reasoning.
 - [GPT4All](https://github.com/nomic-ai/gpt4all) `🌱` `[C++]` `[Local]` - Open-source local chat application that runs LLMs on consumer hardware without cloud dependencies.
 - [Jan](https://github.com/janhq/jan) `🌱` `[TypeScript]` `[Local]` - Open-source ChatGPT alternative that runs 100% offline with local model management and privacy guarantees.
 - [Llamafile](https://github.com/mozilla-ai/llamafile) `🌱` `[C++]` `[Local]` - Distributes LLMs as single executable files with zero setup required from Mozilla.
@@ -801,6 +830,7 @@ Curated newsletters, podcasts, and communities for staying current with AI agent
 - [r/ClaudeAI](https://www.reddit.com/r/ClaudeAI/) `🌱` `[Python]` `[Multi-Agent]` - Reddit community for Claude users sharing agent workflows, prompts, and integration patterns.
 - [r/LangChain](https://www.reddit.com/r/LangChain/) `🌱` `[Python]` `[LangChain]` - Reddit community for agent developers using LangChain, LangGraph, and related frameworks.
 - [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/) `🌱` `[Cloud]` `[IDE]` - Reddit community for self-hosted LLM users sharing local deployment and agent setup guides.
+- [The Agents Index](https://theagentsindex.com) `🔬` `[Cloud]` `[Multi-Agent]` - Compares AI agent tools with sourced pricing, verdicts, and pros/cons in a researched, quality-gated directory.
 - [The Rundown AI](https://www.therundown.ai) `🌱` `[Python]` `[RAG]` - Daily AI digest reaching 600K+ subscribers with concise coverage of agent news and launches.
 
 - [Agents Launchpad](https://launchpad.smartbizcalc.com) `🌱` `[Python]` `[Multi-Agent]` - Community-curated directory of indie AI agents and tools.

upstream/VoltAgent-awesome-ai-agent-papers/catalogue/README.md

@@ -2,9 +2,9 @@
 title: "Awesome AI Agent Papers"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/VoltAgent/awesome-ai-agent-papers/blob/aa50c0c3/README.md
-upstream_sha: aa50c0c3
-imported_at: 2026-07-03
+upstream_source: https://github.com/VoltAgent/awesome-ai-agent-papers/blob/29037d57/README.md
+upstream_sha: 29037d57
+imported_at: 2026-08-17
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -31,7 +31,7 @@ validated: false
 </div>
 
 [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
-![Papers Count](https://img.shields.io/badge/Research%20Papers-363+-b31b1b)
+![Papers Count](https://img.shields.io/badge/Research%20Papers-364+-b31b1b)
 ![Last Update](https://img.shields.io/github/last-commit/VoltAgent/awesome-ai-agent-papers?label=Last%20update)
 <a href="https://github.com/VoltAgent/voltagent">
   <img alt="VoltAgent" src="https://cdn.voltagent.dev/website/logo/logo-2-svg.svg" height="20" />
@@ -61,6 +61,8 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 ### Table of Contents
 
 - [Multi-Agent](#multi-agent) (53)
+- [Memory & RAG](#memory--rag) (58)
+- [Multi-Agent](#multi-agent) (54)
 - [Memory & RAG](#memory--rag) (57)
 - [Eval & Observability](#eval--observability) (80)
 - [Agent Tooling](#agent-tooling) (95)
@@ -69,13 +71,15 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 <br>
 
 <details open id="multi-agent">
-<summary><h3 style="display:inline">Multi-Agent (53)</h3></summary>
+<summary><h3 style="display:inline">Multi-Agent (54)</h3></summary>
 
 <br>
 
 | Paper | arXiv ID |
 |---|:---:|
+| **[Agon: An Autonomous Large-Scale Omnidisciplinary Research System Built on Prompt Economy](https://arxiv.org/pdf/2606.24177)** - Treats prompt engineering as an engineering discipline rather than an art, minimizing human time while expecting maximum output. Carries the smallest prompt footprint among cross-disciplinary autoresearch systems (18 roles, 230.6 KiB total), has run across 10+ research fields without modification, and the longest observed run went 30 days unattended. | <a href="https://arxiv.org/abs/2606.24177"><img src="https://img.shields.io/badge/arXiv-2606.24177-b31b1b.svg" alt="arXiv" /></a> |
 | **[PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation](https://arxiv.org/pdf/2606.05697)** - A persona-conditioned framework that predicts how a specific user would answer UI/UX evaluation questions and explains why in natural language. Trained via contrastive reflection fine-tuning and reflective prompt evolution, reaching human-level realism and generalizing to unseen questions and personas. | <a href="https://arxiv.org/abs/2606.05697"><img src="https://img.shields.io/badge/arXiv-2606.05697-b31b1b.svg" alt="arXiv" /></a> |
+| **[OpenCLAW-P2P v6.0: Resilient Multi-Layer Persistence, Live Reference Verification, and Production-Scale Evaluation of Decentralized AI Peer Review](https://arxiv.org/pdf/2604.19792)** - Presents a decentralized AI peer-review platform where autonomous agents publish, score, verify references, and preserve research papers across a multi-layer storage and retrieval architecture. | <a href="https://arxiv.org/abs/2604.19792"><img src="https://img.shields.io/badge/arXiv-2604.19792-b31b1b.svg" alt="arXiv" /></a> |
 | **[AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing](https://arxiv.org/pdf/2602.17607)** - A multi-agent pipeline that reads a PDE problem description in plain text and writes, debugs, and validates a classical numerical solver end-to-end. Generates spectral and finite-difference code (no neural networks), scoring ~6 orders of magnitude below FNO and CodePDE baselines. | <a href="https://arxiv.org/abs/2602.17607"><img src="https://img.shields.io/badge/arXiv-2602.17607-b31b1b.svg" alt="arXiv" /></a> |
 | **[Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation](https://arxiv.org/abs/2604.09549)** - Evaluates recommender systems via agent-RS interactions. | <a href="https://arxiv.org/abs/2604.09549"><img src="https://img.shields.io/badge/arXiv-2602.06039-b31b1b.svg" alt="arXiv" /></a> |
 | **[CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery](https://arxiv.org/pdf/2604.01658)** - Introduces long-running multi-agent systems that self-evolve via shared persistent memory, asynchronous execution, and heartbeat-based interventions; 3–10× higher improvement rates than fixed evolutionary-search baselines on 10 math/algorithmic/systems tasks. | <a href="https://arxiv.org/abs/2604.01658"><img src="https://img.shields.io/badge/arXiv-2604.01658-b31b1b.svg" alt="arXiv" /></a> |
@@ -137,13 +141,14 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 <br>
 
 <details open id="memory--rag">
-<summary><h3 style="display:inline">Memory & RAG (56)</h3></summary>
+<summary><h3 style="display:inline">Memory & RAG (57)</h3></summary>
 
 <br>
 
 | Paper | arXiv ID |
 |---|:---:|
 | **[Corpus2Skill: Don't Retrieve, Navigate — Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG](https://arxiv.org/pdf/2604.14572)** - Compiles a corpus offline into a hierarchical tree of Agent Skills that the LLM agent navigates at query time, replacing retrieval with skill-tree traversal. | <a href="https://arxiv.org/abs/2604.14572"><img src="https://img.shields.io/badge/arXiv-2604.14572-b31b1b.svg" alt="arXiv" /></a> |
+| **[Semantic Level of Detail for Knowledge Graphs: Discovering Abstraction Boundaries via Spectral Heat Diffusion](https://arxiv.org/pdf/2603.08965)** - Gives an agent a continuous zoom control over a knowledge graph, so it can move between broad and detailed views without hand-tuning a community-detection resolution parameter. Proves the abstraction levels stay consistent as it zooms and shows stable boundary detection on noisy graphs. | <a href="https://arxiv.org/abs/2603.08965"><img src="https://img.shields.io/badge/arXiv-2603.08965-b31b1b.svg" alt="arXiv" /></a> |
 | **[BudgetMem: Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory](https://arxiv.org/pdf/2602.06025v1)** - Investigates routing agent memory queries to different processing tiers based on query difficulty to control the cost-accuracy trade-off at runtime. | <a href="https://arxiv.org/abs/2602.06025v1"><img src="https://img.shields.io/badge/arXiv-2602.06025-b31b1b.svg" alt="arXiv" /></a> |
 | **[Learning to Share: Selective Memory for Efficient Parallel Agentic Systems](https://arxiv.org/pdf/2602.05965v1)** - Proposes a shared memory bank with a learned controller that decides what information is worth passing between parallel agent teams to reduce redundant work. | <a href="https://arxiv.org/abs/2602.05965v1"><img src="https://img.shields.io/badge/arXiv-2602.05965-b31b1b.svg" alt="arXiv" /></a> |
 | **[CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering](https://arxiv.org/pdf/2602.05728v1)** - Explores converting a corpus into atomic QA pairs offline to resolve multi-hop questions with just two LLM calls regardless of hop count. | <a href="https://arxiv.org/abs/2602.05728v1"><img src="https://img.shields.io/badge/arXiv-2602.05728-b31b1b.svg" alt="arXiv" /></a> |
@@ -206,14 +211,16 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 <br>
 
 <details id="eval--observability">
-<summary><h3 style="display:inline">Eval & Observability (80)</h3></summary>
+<summary><h3 style="display:inline">Eval & Observability (81)</h3></summary>
 
 <br>
 
 | Paper | arXiv ID |
 |---|:---:|
 | **[PerspectiveGap: A Benchmark for Multi-Agent Orchestration Prompting](https://arxiv.org/pdf/2606.08878)** - A 110-scenario benchmark testing whether LLMs can compose orchestration prompts that distribute context to sub-agents without information leakage. Across 10 topologies and 27 commercial models, GPT-5.5 leads with 62% pass rate while the average is 14.9%. | <a href="https://arxiv.org/abs/2606.08878"><img src="https://img.shields.io/badge/arXiv-2606.08878-b31b1b.svg" alt="arXiv" /></a> |
+| **[RewardHarness: Self-Evolving Agentic Post-Training](https://arxiv.org/pdf/2605.08703)** - Evolves a library of scoring skills and tools from preference examples, then uses a frozen vision-language sub-agent to evaluate image edits and produce a reward for GRPO training. | <a href="https://arxiv.org/abs/2605.08703"><img src="https://img.shields.io/badge/arXiv-2605.08703-b31b1b.svg" alt="arXiv" /></a> |
 | **[ClawBench: Evaluating Browser Agents on Live Production Websites with Submission-Interception](https://arxiv.org/abs/2604.08523)** - Benchmarks browser agents on 283 everyday tasks (V1 153 + V2 130) across 163 live production sites, with a Chrome-extension plus CDP layer that blocks only the final write request so agents can run end-to-end on real sites without real-world side effects. Two-stage scoring (interception + LLM judge); leaderboard at https://claw-bench.com. | <a href="https://arxiv.org/abs/2604.08523"><img src="https://img.shields.io/badge/arXiv-2604.08523-b31b1b.svg" alt="arXiv" /></a> |
+| **[StructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputs](https://arxiv.org/pdf/2505.20139)** - Benchmarks structured-output generation and cross-format conversion across 18 text and renderable formats, with syntax, structural, and visual evaluation checks. | <a href="https://arxiv.org/abs/2505.20139"><img src="https://img.shields.io/badge/arXiv-2505.20139-b31b1b.svg" alt="arXiv" /></a> |
 | **[From Features to Actions: Explainability in Traditional and Agentic AI Systems](https://arxiv.org/pdf/2602.06841v1)** - Compares attribution-based explanations with trace-based diagnostics across static and agentic settings to study how explainability methods translate to multi-step agent trajectories. | <a href="https://arxiv.org/abs/2602.06841v1"><img src="https://img.shields.io/badge/arXiv-2602.06841-b31b1b.svg" alt="arXiv" /></a> |
 | **[Agentic Uncertainty Reveals Agentic Overconfidence](https://arxiv.org/pdf/2602.06948v1)** - Investigates whether agents can accurately predict their own success rates in agentic tasks. | <a href="https://arxiv.org/abs/2602.06948v1"><img src="https://img.shields.io/badge/arXiv-2602.06948-b31b1b.svg" alt="arXiv" /></a> |
 | **[AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents](https://arxiv.org/pdf/2602.06855v1)** - Introduces 20 research tasks from real ML papers covering idea generation, experiments, and refinement for benchmarking science agents. | <a href="https://arxiv.org/abs/2602.06855v1"><img src="https://img.shields.io/badge/arXiv-2602.06855-b31b1b.svg" alt="arXiv" /></a> |
@@ -258,6 +265,7 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 | **[The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution](https://arxiv.org/pdf/2601.15075v2)** - Proposes a hierarchical framework for general agentic attribution that identifies internal factors driving agent actions through temporal likelihood dynamics and perturbation-based analysis. | <a href="http://arxiv.org/abs/2601.15075v2"><img src="https://img.shields.io/badge/arXiv-2601.15075-b31b1b.svg" alt="arXiv" /></a> |
 | **[Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering](https://arxiv.org/pdf/2601.14470v1)** - Analyzes token consumption patterns across software development lifecycle stages in a multi-agent system to identify where tokens are consumed and which stages drive cost. | <a href="http://arxiv.org/abs/2601.14470v1"><img src="https://img.shields.io/badge/arXiv-2601.14470-b31b1b.svg" alt="arXiv" /></a> |
 | **[APEX-Agents](https://arxiv.org/pdf/2601.14242v2)** - Introduces a benchmark of 480 long-horizon, cross-application productivity tasks created by investment banking analysts, consultants, and lawyers for evaluating AI agent capabilities in realistic work environments. | <a href="http://arxiv.org/abs/2601.14242v2"><img src="https://img.shields.io/badge/arXiv-2601.14242-b31b1b.svg" alt="arXiv" /></a> |
+| **[DRIFT: Detecting Representational Inconsistencies for Factual Truthfulness](https://arxiv.org/pdf/2601.14210)** - Lightweight probes (3M-37M params) trained on middle-layer hidden states catch factually wrong generations before they reach the user, at less than 0.1% overhead. Up to 13 AUROC points above final-layer baselines, and the probes transfer across datasets without retraining. | <a href="https://arxiv.org/abs/2601.14210"><img src="https://img.shields.io/badge/arXiv-2601.14210-b31b1b.svg" alt="arXiv" /></a> |
 | **[CooperBench: Why Coding Agents Cannot be Your Teammates Yet](https://arxiv.org/pdf/2601.13295v2)** - Introduces a benchmark of 600+ collaborative coding tasks to evaluate whether coding agents can coordinate as effective teammates under various coordination structures. | <a href="http://arxiv.org/abs/2601.13295v2"><img src="https://img.shields.io/badge/arXiv-2601.13295-b31b1b.svg" alt="arXiv" /></a> |
 | **[Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?](https://arxiv.org/pdf/2601.13227v1)** - Investigates how RAG systems can game nugget-based LLM judge evaluations through metric overfitting, demonstrating near-perfect scores when evaluation elements are leaked or predictable. | <a href="http://arxiv.org/abs/2601.13227v1"><img src="https://img.shields.io/badge/arXiv-2601.13227-b31b1b.svg" alt="arXiv" /></a> |
 | **[Replayable Financial Agents: A Determinism-Faithfulness Assurance Harness for Tool-Using LLM Agents](https://arxiv.org/pdf/2601.15322v1)** - Introduces the Determinism-Faithfulness Assurance Harness for measuring trajectory determinism and evidence-conditioned faithfulness in tool-using LLM agents across 74 configurations and 12 models. | <a href="http://arxiv.org/abs/2601.15322v1"><img src="https://img.shields.io/badge/arXiv-2601.15322-b31b1b.svg" alt="arXiv" /></a> |
@@ -299,12 +307,14 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 <br>
 
 <details id="agent-tooling">
-<summary><h3 style="display:inline">Agent Tooling (96)</h3></summary>
+<summary><h3 style="display:inline">Agent Tooling (97)</h3></summary>
 
 <br>
 
 | Paper | arXiv ID |
 |---|:---:|
+| **[SCATE: Learning to Supervise Coding Agents for Cost-Effective Test Generation](https://arxiv.org/pdf/2607.08983)** - Replaces the human who keeps prodding a coding agent to write more tests with a contextual bandit that picks the next testing action from current coverage and class testability signals. Driving GEMINI-CLI, it reaches 32.3% higher line coverage and 30.9% higher branch coverage than the agent on its own, and it learns a different policy when the same setup drives CLAUDE CODE. | <a href="https://arxiv.org/abs/2607.08983"><img src="https://img.shields.io/badge/arXiv-2607.08983-b31b1b.svg" alt="arXiv" /></a> |
+| **[Ouroboros: A Self-Developing Frontier Coding Agent with Reviewed Core Evolution](https://arxiv.org/pdf/2608.08311)** - Documents a coding-agent harness that can update its tools, prompts, context assembly, and core code through reviewed commits, with a 161-day live deployment. Reports frozen-snapshot results of 86.74% on Terminal-Bench 2.1 and 90.69% on OSWorld-Verified. | <a href="https://arxiv.org/abs/2608.08311"><img src="https://img.shields.io/badge/arXiv-2608.08311-b31b1b.svg" alt="arXiv" /></a> |
 | **[On Effectiveness and Efficiency of Agentic Tool-calling and RL Training](https://arxiv.org/pdf/2606.00135)** - Find that current agentic tool-calling benchmarks like BFCL are quite brittle: system prompt, multi-turn template or even pure random seeds could have huge influence on the final performance. | <a href="https://arxiv.org/abs/2606.00135"><img src="https://img.shields.io/badge/arXiv-2602.06875-b31b1b.svg" alt="arXiv" /></a> |
 | **[TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging](https://arxiv.org/pdf/2602.06875v1)** - Proposes a multi-agent observe-analyze-repair loop that uses runtime traces to find and fix bugs in LLM-generated code. | <a href="https://arxiv.org/abs/2602.06875v1"><img src="https://img.shields.io/badge/arXiv-2602.06875-b31b1b.svg" alt="arXiv" /></a> |
 | **[Generative Ontology: When Structured Knowledge Learns to Create](https://arxiv.org/pdf/2602.05636v1)** - Explores constraining LLM generation with executable schemas and multi-agent roles to produce structurally valid yet creative outputs. | <a href="https://arxiv.org/abs/2602.05636v1"><img src="https://img.shields.io/badge/arXiv-2602.05636-b31b1b.svg" alt="arXiv" /></a> |