A new native-runtime benchmark reveals that current frontier AI agents succeed on at most 62 percent of realistic long-horizon CLI tasks.
Deepseek-v3.2: Pushing the frontier of open large language models
7 Pith papers cite this work. Polarity classification is still indexing.
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2026 7representative citing papers
AutoResearchBench is a new benchmark showing top AI agents achieve under 10% success on complex scientific literature discovery tasks that demand deep comprehension and open-ended search.
Region4Web shows that shifting web agent observation to functional regions instead of element-level granularity produces shorter, more effective state representations and raises task success on WebArena across multiple LLMs and agent methods.
QuasiMoTTo uses quasi-Monte Carlo to produce correlated yet marginally correct samples from language models, matching i.i.d. pass@k with 25-47% fewer samples on reasoning benchmarks and 50% fewer RL training steps.
Frontier LRMs match human game-learning behavior and predict fMRI signals an order of magnitude better than RL or Bayesian agents because of their in-context game-state representations.
LERA is a retrieve-then-generate auction system that refines ad candidate ranking with LLM logits and applies a threshold-aware critical-value payment rule to maintain truthfulness in chatbot ad insertion.
JoyAI-LLM Flash delivers a 48B MoE LLM with 2.7B active parameters per token via FiberPO RL and dense multi-token prediction, released with checkpoints on Hugging Face.
citing papers explorer
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WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation
A new native-runtime benchmark reveals that current frontier AI agents succeed on at most 62 percent of realistic long-horizon CLI tasks.
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AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery
AutoResearchBench is a new benchmark showing top AI agents achieve under 10% success on complex scientific literature discovery tasks that demand deep comprehension and open-ended search.
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Region4Web: Rethinking Observation Space Granularity for Web Agents
Region4Web shows that shifting web agent observation to functional regions instead of element-level granularity produces shorter, more effective state representations and raises task success on WebArena across multiple LLMs and agent methods.
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QuasiMoTTo: Quasi-Monte Carlo Test-Time Scaling
QuasiMoTTo uses quasi-Monte Carlo to produce correlated yet marginally correct samples from language models, matching i.i.d. pass@k with 25-47% fewer samples on reasoning benchmarks and 50% fewer RL training steps.
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Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners
Frontier LRMs match human game-learning behavior and predict fMRI signals an order of magnitude better than RL or Bayesian agents because of their in-context game-state representations.
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LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots
LERA is a retrieve-then-generate auction system that refines ad candidate ranking with LLM logits and applies a threshold-aware critical-value payment rule to maintain truthfulness in chatbot ad insertion.
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JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency
JoyAI-LLM Flash delivers a 48B MoE LLM with 2.7B active parameters per token via FiberPO RL and dense multi-token prediction, released with checkpoints on Hugging Face.