[upstream-sync] README.md from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@17b14496 [catalogue]

Reviewed-on: #17

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/842f796f/README.md
-upstream_sha: 842f796f
-imported_at: 2026-06-28
+upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/17b14496/README.md
+upstream_sha: 17b14496
+imported_at: 2026-07-13
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -18,7 +18,7 @@ validated: false
 # Awesome AI Agents 2026
 
 [![Awesome](https://awesome.re/badge.svg)](https://github.com/sindresorhus/awesome)
-[![GitHub stars](https://img.shields.io/github/stars/ARUNAGIRINATHAN-K/awesome-ai-agents-2026?style=social)](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/stargazers)
+[![GitHub stars](https://img.shields.io/github/stars/ARUNAGIRINATHAN-K/awesome-ai-agents-2026?style=social)](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026)
 ![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)
 [![Last Commit](https://img.shields.io/github/last-commit/ARUNAGIRINATHAN-K/awesome-ai-agents-2026)](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/commits/main)
 ![Tools Listed](https://img.shields.io/badge/tools_listed-470%2B-blue?style=flat-square)
@@ -139,6 +139,7 @@ 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.
@@ -155,7 +156,9 @@ validated: false
 - [Qdrant](https://github.com/qdrant/qdrant) `🌱` `[Rust]` `[Vector DB]` - High-performance vector similarity search engine with rich payload filtering for agent memory.
 - [RAGFlow](https://github.com/infiniflow/ragflow) `🌱` `[Python]` `[RAG]` - Open-source RAG engine with agent capabilities and deep document understanding for knowledge bases.
 - [SimpleMem](https://github.com/aiming-lab/SimpleMem) `🌱` `[Python]` `[Multimodal]` - Efficient lifelong memory for LLM agents supporting both text and multimodal inputs.
+- [Superdense](https://github.com/Nimrobo/superdense) `🔬` `[TypeScript]` `[Memory]` - Gives coding agents persistent memory of what worked across sessions, tracked against real-world outcomes.
 - [Supermemory](https://github.com/supermemoryai/supermemory) `🌱` `[TypeScript]` `[Vector DB]` - Extremely fast and scalable memory engine and API designed for the AI era.
+- [Tree Ring Memory](https://github.com/TerminallyLazy/Tree-Ring-Memory) `🔬` `[Rust]` `[Memory]` - Manages local agent memory with recall, forgetting, audit trails, and session consolidation.
 - [Vestige](https://github.com/samvallad33/vestige) `🌱` `[Rust]` `[MCP]` - Provides local-first memory for coding agents with FSRS-6 retention, active forgetting, and correction tools.
 - [Weaviate](https://github.com/weaviate/weaviate) `🌱` `[Go]` `[Vector DB]` - Stores and searches vector embeddings with hybrid keyword and semantic retrieval for agent knowledge.
 - [Zep](https://github.com/getzep/zep) `🌱` `[Python]` `[Multi-Agent]` - Enriches agent long-term memory with automatic summarization, entity extraction, and search.
@@ -164,6 +167,7 @@ validated: false
 
 - [AgentVerse](https://github.com/OpenBMB/AgentVerse) `🌱` `[Python]` `[Multi-Agent]` - Framework for building custom multi-agent environments to accomplish collaborative tasks.
 - [EvoAgentX](https://github.com/EvoAgentX/EvoAgentX) `🌱` `[Python]` `[Multi-Agent]` - Evaluates and evolves agentic workflows over time using automatic optimization.
+- [Hivekeep](https://github.com/MarlBurroW/hivekeep) `🔬` `[TypeScript]` `[Multi-Agent]` - Runs a team of specialized self-hosted agents that collaborate, share memory, and build their own tools.
 - [Hivemoot](https://github.com/hivemoot/hivemoot) `🚀` `[Python]` `[GitHub]` - Autonomous agent teams that collaboratively build software on GitHub.
 - [MetaGPT](https://github.com/FoundationAgents/MetaGPT) `🌱` `[Python]` `[Multi-Agent]` - Simulates a full software company workflow from requirements to PRs using role-playing agents.
 - [NarraNexus](https://github.com/NetMindAI-Open/NarraNexus) `🌱` `[Python]` `[Multi-Agent]` - Ready-to-run AI agent team workspace by NetMind.AI whose agents remember, collaborate, and use tools from day one.
@@ -224,6 +228,7 @@ 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.
 - [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.
@@ -253,6 +258,7 @@ 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.
+- [LLMGraph](https://llmgraph.ai) `🔬` `[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.
@@ -297,6 +303,7 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
 - [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.
 - [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.
@@ -305,11 +312,13 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
 
 Frontend workspaces and chat interfaces with built-in agent plugins and tool-use capabilities.
 
+- [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.
 - [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.
 - [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.
 
 ## Agent Deployment and Hosting
@@ -392,6 +401,7 @@ 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.
@@ -426,6 +436,7 @@ 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.
@@ -563,6 +574,7 @@ AI agents that automate customer support, CRM workflows, sales outreach, and tic
 ### Sales and Outreach Agents
 
 - [Apollo.io](https://www.apollo.io) `🌱` `[Cloud]` `[Multi-Agent]` - AI prospecting platform with 275M+ contacts, lead scoring, and automated email sequencing.
+- [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.
@@ -602,6 +614,7 @@ 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.
@@ -744,6 +757,7 @@ Tools for running LLMs locally and self-hosting AI agent platforms with full pri
 - [LM Studio](https://lmstudio.ai) `🌱` `[TypeScript]` `[Local]` - Desktop application for running local LLMs with a polished UI across all major platforms.
 - [LocalAI](https://github.com/mudler/LocalAI) `🚀` `[Go]` `[OpenAI]` - Drop-in OpenAI API replacement that runs models locally without requiring a GPU.
 - [Ollama](https://github.com/ollama/ollama) `🚀` `[Go]` `[GitHub]` - Run LLMs locally with a dead-simple CLI interface and 162K+ GitHub stars.
+- [RustFox](https://github.com/chinkan/RustFox) `🔬` `[Rust]` `[MCP]` - Self-hosted Telegram AI assistant written in Rust with sandboxed tool execution, MCP integration, and multi-agent orchestration.
 - [vLLM](https://github.com/vllm-project/vllm) `🚀` `[Python]` `[Local]` - High-throughput LLM serving engine with PagedAttention for production-grade local inference.
 - [Yao Agents](https://github.com/YaoApp/yao) `🌱` `[Go]` `[MCP]` - Local-first AI execution platform with Docker sandbox isolation, BYOK model configuration, MCP support, 5-stage Pipeline, and multi-platform messaging via WeChat, Feishu, DingTalk, Telegram, and Discord.
 
@@ -763,6 +777,7 @@ Frameworks and tools for AI risk management, regulatory compliance, and governan
 
 AI agents specialized in penetration testing, vulnerability discovery, threat detection, and security analysis.
 
+- [Dark-Moon](https://github.com/ASCIT31/Dark-Moon) `🌱` `[Python]` `[MCP]` - Autonomous AI penetration testing platform orchestrating 80+ offensive tools via MCP across web, cloud, Active Directory, Kubernetes, and API targets.
 - [Microsoft Security Copilot](https://www.microsoft.com/en-us/security/business/ai-machine-learning/microsoft-security-copilot) `🚀` `[Cloud]` `[Microsoft]` - Enterprise threat detection and incident response AI integrated across Microsoft security products.
 - [PentestGPT](https://github.com/GreyDGL/PentestGPT) `🌱` `[Python]` `[CLI]` - GPT-powered penetration testing tool with automated reasoning for vulnerability assessment.
 - [YAWNING TITAN](https://github.com/dstl/YAWNING-TITAN) `🌱` `[Python]` `[Graph-Based]` - Graph-based cybersecurity simulation environment for training and testing defensive AI agents.

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/d467d641/README.md
-upstream_sha: d467d641
-imported_at: 2026-06-26
+upstream_source: https://github.com/VoltAgent/awesome-ai-agent-papers/blob/aa50c0c3/README.md
+upstream_sha: aa50c0c3
+imported_at: 2026-07-03
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -75,6 +75,7 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 
 | Paper | arXiv ID |
 |---|:---:|
+| **[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> |
 | **[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> |
@@ -211,6 +212,7 @@ Hundreds of papers are published on arXiv every week, and a growing number of th
 
 | 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> |
 | **[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> |
 | **[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> |
@@ -297,12 +299,13 @@ 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 (95)</h3></summary>
+<summary><h3 style="display:inline">Agent Tooling (96)</h3></summary>
 
 <br>
 
 | Paper | arXiv ID |
 |---|:---:|
+| **[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> |
 | **[Structured Context Engineering for File-Native Agentic Systems](https://arxiv.org/pdf/2602.05447v1)** - Tests how context format (YAML, JSON, Markdown) affects agent accuracy across 9,649 experiments in file-native agentic systems. | <a href="https://arxiv.org/abs/2602.05447v1"><img src="https://img.shields.io/badge/arXiv-2602.05447-b31b1b.svg" alt="arXiv" /></a> |