SelSkill applies dual-granularity preference learning to selective skill-or-skip decisions, improving task success by 10.9 points and execution precision by 29.1 points on ALFWorld with Qwen3-8B.
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Vitabench: Benchmarking llm agents with versatile interactive tasks in real-world applications
13 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 13representative citing papers
VitaBench 2.0 introduces a benchmark for long-term personalized and proactive agent behavior, with results indicating substantial gaps in current frontier LLMs.
AgentEscapeBench is a benchmark of 270 tasks across five difficulty tiers that measures LLM agents' ability to manage long-range tool dependencies, state tracking, and intermediate result propagation, revealing sharp performance drops with increasing depth.
MAD-OPD recasts on-policy distillation teachers as a debating collective to supply better supervision, lifting agentic and code performance over single-teacher OPD across multiple model sizes.
WildRoadBench is a new dual-track benchmark on professionally annotated wild UAV road-damage images showing closed-source VLMs lead but leave over half the AP_50 metric on the table while agents lag and open-source models collapse on small targets.
Missing old logits in async agentic RL entangle discrepancy and staleness terms in PPO off-policy correction; exact acquisition methods and revised PPO-EWMA restore decoupled updates with reported gains in speed and performance.
CL-bench Life shows frontier language models achieve only 13.8% average success on real-life context tasks, with the best model at 19.3%.
A 35B MoE agent model trained on 45K-token trajectories via three-stage SFT and domain-routed distillation achieves leading or competitive scores against 1T models on SEAL-0, IFBench, HiPhO, FrontierScience-Olympiad and MolBench-Bind.
NoisyAgent trains LLM agents with controlled user and tool noise to improve robustness in stochastic environments while also boosting clean-benchmark performance.
MM-ToolBench introduces 100 closed-loop multimodal tasks across two domains with 27 MCP servers and 324 tools, where agents must execute, inspect artifacts, and revise before final output.
UserGPT introduces a generative LLM framework with a behavior simulation engine, semantization module, and DF-GRPO post-training that scores 0.7325 on tag prediction and 0.7528 on summary generation on HPR-Bench while compressing records by up to 97.9%.
Technical report announcing Ling-2.6 and Ring-2.6 models with hybrid linear attention, evolutionary CoT, and KPop RL for efficient agentic intelligence at scale.
Seed2.0 model series reports gains in reasoning, visual understanding, search, and reliability on intricate long-horizon tasks via an internal evaluation system.
citing papers explorer
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AgentEscapeBench: Evaluating Out-of-Domain Tool-Grounded Reasoning in LLM Agents
AgentEscapeBench is a benchmark of 270 tasks across five difficulty tiers that measures LLM agents' ability to manage long-range tool dependencies, state tracking, and intermediate result propagation, revealing sharp performance drops with increasing depth.
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CL-bench Life: Can Language Models Learn from Real-Life Context?
CL-bench Life shows frontier language models achieve only 13.8% average success on real-life context tasks, with the best model at 19.3%.