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AI Agent Papers·2026
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From Evidence to Action: How Tool-Using Agents Fail

Hongzhan Lin, Shidong Cao +4·Princeton University, National University of Singapore +2

Analyzes where LLM-based agents break the evidence-to-action chain and introduces SafeActBench, a 656-case, provenance-bound benchmark and deterministic evaluator to diagnose failures in investigation, timing, single-action execution, and multi-step workflows.

#evaluation#benchmark#ai-agent#agent-skills#LLM+1
AI Agent Papers·2026
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Recursive Game Creator: An Agentic Product-Level Experience-Oriented Game Harness

Jiajun Chen, Haoyu Wu +2·HKU MMLab, The University of Hong Kong +1

Iteratively refines agent-generated games using a closed loop of Designer, Builder, Coding‑Native Player, and Experience‑Oriented Reviewer — collects programmatic gameplay trajectories and trajectory+visual evaluations to evolve prototypes into player-focused, product-level games; shows measurable gains on GameCraft-Bench and GameASG-Bench.

#coding-agents#agent-skills#evaluation#benchmarks#RL+3
AI Agent Papers·2026
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nanoMuse: An Open-Source Personal Agent for Every Device You Own

Guangyi Liu, Yong Liu +1

Runs an open-source personal software agent across a user's devices to perform actions, keep readable provenance-backed memories, and coordinate via a self-hostable relay. Ships minimal end-to-end clients (mobile, desktop, web) under GPL-3.0; model-agnostic and designed for inspectability and device

#ai-agent#agent-skills#github#paper#mcp-server+10
AI Agent Papers·2026
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Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?

Yibo Li, Jinhang Qiu +5

Measures how effectively LLM agents learn from interaction by playing 20 text-based games with novel or counterintuitive hidden rules, providing deterministic feedback, episode-wise scoring, and controlled variations to test retention and transfer.

#benchmarks#LLM#ai-agent#agent-skills#evaluation
AI Agent Papers·2026
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AgentGarten: Code Worlds for Evolving Agents

Jiawei Chi, Shangchen Miao +12

Creates programmable, real-time interactive code-based environments by separating deterministic simulator state from a shared neural video renderer so agents perceive, interact, and iteratively evolve via distilled playbooks; introduces Adversarial Forcing to distill a geometry-conditioned renderer for responsive visual feedback.

#embodied-ai#video#distillation#agent-skills#RL+5
Hugging Face
AI Dataset·2026
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openai/math as Harbor environments

FineEnvs, OpenAI +3

Provides 369 Harbor sandbox tasks ported from OpenAI's openai/math: each task is a Lean theorem with missing `sorry` proofs that an agent must complete, graded by a strict Comparator exact-match verifier. Includes task definitions, generator, and manifest for RL/code-agent evaluation.

#math#rl#coding-agents#huggingface#benchmark+3
AI Agent Papers·2026
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SuperNav: An Agentic Navigation System for Any Task in Any Scene

Jinkai Zhang, Jingyi Xu +6·Zhejiang University, Shenzhen University +1

Lets a pretrained multimodal LLM interpret navigation requests and orchestrate motion via tool calls for generalist robot navigation across unfamiliar scenes. Key features: an agent harness with Navigation Skills, a unified visual-point interface, task-progress tracking, and tool-based motion execution without navigation-specific fine-tuning.

#embodied-ai#multimodal#vision#robotics#agent-skills+2
Hugging Face
AI Model·2026
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Underdog Saluki 27B 1.0

ConwayResearch (Underdog), Underdog AI +2

A 2-bit quantized GGUF of Qwen3.8-27B that fits under 8 GB and runs on stock llama.cpp while preserving function/tool-calling behavior; includes an optional small vision add-on and is tuned for agent/tool workflows (Apache-2.0).

#gguf#llama.cpp#quantization#qwen#benchmarks+4
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