RS-Claw enables remote sensing agents to actively explore tools via hierarchical skill trees, achieving up to 86% token compression and outperforming flat registration and RAG baselines on Earth-Bench.
Toolgen: Unified tool retrieval and calling via generation
9 Pith papers cite this work. Polarity classification is still indexing.
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ToolHijacker optimizes malicious tool documents via a two-phase strategy to hijack LLM agents' tool selection in no-box settings.
NTILC replaces in-context tool registry lookup with learned latent retrieval using a signature-aware composite loss, reducing context consumption by over 95% and latency by up to 74%.
LLM agents trained with a task-success reward on self-generated knowledge can spontaneously explore and adapt to new environments without any rewards or instructions at inference, yielding 20% gains on web tasks and allowing a 14B model to beat Gemini-2.5-Flash.
SkillDisCo distills reusable PFSM subgraphs from successful agent traces and compiles them into callable procedural skills, improving success rates and reducing turns on ALFWorld and WebArena.
QLoRA fine-tuning on tool-use data enables 4B-parameter models to perform structured planning without tool catalogs in prompts, outperforming informed baselines on AssetOpsBench while reducing input length by 82.6%.
A survey that taxonomizes threats to agentic AI, reviews benchmarks and evaluation methods, discusses technical and governance defenses, and identifies open challenges.
citing papers explorer
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RS-Claw: Progressive Active Tool Exploration via Hierarchical Skill Trees for Remote Sensing Agents
RS-Claw enables remote sensing agents to actively explore tools via hierarchical skill trees, achieving up to 86% token compression and outperforming flat registration and RAG baselines on Earth-Bench.
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Prompt Injection Attack to Tool Selection in LLM Agents
ToolHijacker optimizes malicious tool documents via a two-phase strategy to hijack LLM agents' tool selection in no-box settings.
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NTILC: Neural Tool Invocation via Learned Compression
NTILC replaces in-context tool registry lookup with learned latent retrieval using a signature-aware composite loss, reducing context consumption by over 95% and latency by up to 74%.
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Training LLM Agents for Spontaneous, Reward-Free Self-Evolution via World Knowledge Exploration
LLM agents trained with a task-success reward on self-generated knowledge can spontaneously explore and adapt to new environments without any rewards or instructions at inference, yielding 20% gains on web tasks and allowing a 14B model to beat Gemini-2.5-Flash.
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SKILL-DISCO: Distilling and Compiling Agent Traces into Reusable Procedural Skills
SkillDisCo distills reusable PFSM subgraphs from successful agent traces and compiles them into callable procedural skills, improving success rates and reducing turns on ALFWorld and WebArena.
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Internalizing Tool Knowledge in Small Language Models via QLoRA Fine-Tuning
QLoRA fine-tuning on tool-use data enables 4B-parameter models to perform structured planning without tool catalogs in prompts, outperforming informed baselines on AssetOpsBench while reducing input length by 82.6%.
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Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges
A survey that taxonomizes threats to agentic AI, reviews benchmarks and evaluation methods, discusses technical and governance defenses, and identifies open challenges.
- FitText: Evolving Agent Tool Ecologies via Memetic Retrieval
- FinTrace: Holistic Trajectory-Level Evaluation of LLM Tool Calling for Long-Horizon Financial Tasks