The Archive

In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic action and numerical parameters. In such settings Reinforcement Learning algorithms typically determine parameters with one-shot estimators, which makes their training sample inefficient. Though in most PAMDP environments explicit but incomplete knowledge (e.g., rules, safety constraints, or expert heuristics) is available, it is rarely directly used to increase the sample-efficiency of training Reinforcement Learning agents. We step into this gap...

Framework evolves evaluation metrics alongside agent skills using evolutionary search over drawback detectors, enabling self-improving systems without pre-defined oracles.

Internet of Agentic Things (IoAT) framework integrates autonomous AI agents with IoT, cyber-physical systems, and edge computing for closed-loop orchestration.

Controlled study finds GRPO RL fails to improve 4B–8B scale web agents over supervised baselines across learning rate and hyperparameter grids, questioning RL value at small scale.

MM-ToolSandBox: benchmark with 500+ tools across 16 domains for evaluating visually grounded multi-turn tool-calling agents.

Safety vulnerability: distributed backdoors in multi-agent LLM systems bypass local monitors by splitting harmful payloads across agents.

Automated red-teaming system discovers reusable vulnerability patterns in production LLM agents (Claude Code, Codex) operating on untrusted content.

"Context bombing" tricks hacking agents into shutting down before they can do harm.

Empirical audit reveals trained distributional RL agents' risk estimates often violate first-order stochastic dominance.

Simon Willison examines DRI (Directly Responsible Individual) concept from Apple/GitLab in context of LLM agents, arguing humans must retain accountability.

VEXAIoT: multi-agent LLM framework for autonomous IoT vulnerability discovery and exploitation testing.

QANTA 2026 submission: multimodal QA agent with confidence calibration for incremental quizbowl under efficiency constraints.

Biomolecular structure prediction and co-folding with models like OpenFold3 are now mainstream, large-scale workloads powering drug discovery and protein... Biomolecular structure prediction and co-folding with models like OpenFold3 are now mainstream, large-scale workloads powering drug discovery and protein design. Increasingly, they’re driven end-to-end by AI agents. For an agent to run that pipeline well, every step needs to be fast and scalable: Multiple Sequence Alignment (MSA) generation, co-folding inference, serving, and multi-GPU scale-out. Source

Lyzr, a startup that builds AI agents for enterprises, used its own AI agent to raise a $100 million round — proof, evidently, that the product actually works.

UniClawBench introduces a capability-factored benchmark for evaluating proactive agents on real-world tool-use tasks beyond sandboxed single-turn settings.

Proactive memory agent running alongside action agent mitigates behavioral state decay in long-horizon tasks via structured memory bank updates.

SolarChain-Eval benchmarks autonomous agents in decentralized energy markets with physics constraints and trustworthiness metrics.

Marketplace simulation with DeepSeek-V3 agents tests formal mechanisms for maintaining trade stability against adversarial defection.

SMetric proposes session-centric LLM scheduling for agentic workloads with 80%+ KV-cache reuse, shifting optimization from latency to tokens-per-second.

Agon trains reasoning models via competitive RL where two agents grade each other's solutions, incentivizing better thinking vs. longer traces.

SkillCenter releases 216,938 structured skills across 24 domains from peer-reviewed sources and GitHub for autonomous agent execution.

The round, led by Radical Ventures, values the two-year old startup at $1 billion.

Presto is an open source, distributed SQL engine for running fast, interactive queries on very large datasets. On NVIDIA GPUs, Presto delivers peak performance... Presto is an open source, distributed SQL engine for running fast, interactive queries on very large datasets. On NVIDIA GPUs, Presto delivers peak performance for analytical query workloads and provides low latency for users and agents. GPU-accelerated Presto brings low latency to your analytical workloads, keeping you and your agents unblocked and iterating as fast as possible. Source

Empirical study on capacity allocation across hierarchical search agent roles (delegation, execution, generation) in multi-agent LLM systems.

Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are... Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are expensive. Fine-tuning offers one way to address this problem. Smaller or more efficient open models starting with lower accuracy are taught to perform better with specific agents. However, fine-tuning requires expertise and hardware for… Source

Action-graded severity scale for agent red-teaming replaces binary attack-success metrics with 7-level ordinal harm rubric, enabling nuanced risk assessment of tool-using AI compromise.

Self-evolving LLM agents with biased reward signals fail to retire bad skills, disabling safety constraints in skill libraries.

RLVP: Reward function design for real-world agents requiring path constraints and outcome-neutral safety rules beyond reward maximization.

Tool-using LLM agents silently violate deployed policies via well-formed tool calls that bypass domain constraints; 78% of failures undetected.