BehaviorBench is a benchmark for foundation models on behavioral tasks that reveals fine-tuned behavioral models outperform general models on distributional alignment while general models lead on individual-level accuracy.
Gtbench: Uncovering the strategic reasoning limitations of llms via game-theoretic evaluations
10 Pith papers cite this work. Polarity classification is still indexing.
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RNG-Bench evaluates MLLMs on hidden-observation reconstruction in non-Markov games, finds forgetting as the dominant error source, and shows fine-tuning on optimal rollouts improves performance with transfer to other benchmarks.
Introduces Age of LLM benchmark pitting LLMs in a 13x7 grid game with fog of war, diplomacy, and JSON reliability constraints, reporting nuclear rush dominance in 54 matches and a weak reliability-win link.
DemoEvolve bootstraps harness evolution with demonstrations to achieve more stable and effective edits than self-rollout search in sparse-feedback environments like Balatro.
CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
VS-Bench is a new benchmark of ten visual multi-agent environments that measures VLMs on element recognition, next-action prediction, and normalized episode return, showing strong perception but large gaps in reasoning and decision-making with the best model at 46.6% prediction accuracy and 31.4% of
This survey categorizes agentic environments for LLMs by eight attributes and domains, introduces symbolic and neural synthesis paradigms with evaluation, and outlines four agent evolution pathways plus three environment evolution paradigms.
The paper identifies four missing interfaces (data autolabelling, embodiment retargeting, physics-grounded world models, and video-based reward inference) as the central bottleneck beyond VLA scaling for robot intelligence.
A game-theoretic heterogeneous multi-agent architecture with three cloud LLMs and a local verifier achieves 77.2% F1, 100% recall, and 3x speedup for code vulnerability detection at $0.002 per sample on the NIST Juliet suite.
Proposes a symbolic feedback-driven iterative self-refinement framework for LLM long-horizon planning that maps symbols to natural language, uses a verifier for error correction, and a plan recognizer for goal reachability, with abstract-level claims of improved feasibility and correctness.
citing papers explorer
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BehaviorBench: Benchmarking Foundation Models for Behavioral Science Tasks
BehaviorBench is a benchmark for foundation models on behavioral tasks that reveals fine-tuned behavioral models outperform general models on distributional alignment while general models lead on individual-level accuracy.
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Beyond the Current Observation: Evaluating Multimodal Large Language Models in Controllable Non-Markov Games
RNG-Bench evaluates MLLMs on hidden-observation reconstruction in non-Markov games, finds forgetting as the dominant error source, and shows fine-tuning on optimal rollouts improves performance with transfer to other benchmarks.
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Age of LLM: A Strategic 1v1 Benchmark for Reasoning, Diplomacy and Reliability of Large Language Models under Fog of War
Introduces Age of LLM benchmark pitting LLMs in a 13x7 grid game with fog of war, diplomacy, and JSON reliability constraints, reporting nuclear rush dominance in 54 matches and a weak reliability-win link.
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DemoEvolve: Overcoming Sparse Feedback in Agentic Harness Evolution with Demonstrations
DemoEvolve bootstraps harness evolution with demonstrations to achieve more stable and effective edits than self-rollout search in sparse-feedback environments like Balatro.
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Common-agency Games for Multi-Objective Test-Time Alignment
CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
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VS-Bench: Evaluating VLMs for Strategic Abilities in Multi-Agent Environments
VS-Bench is a new benchmark of ten visual multi-agent environments that measures VLMs on element recognition, next-action prediction, and normalized episode return, showing strong perception but large gaps in reasoning and decision-making with the best model at 46.6% prediction accuracy and 31.4% of
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Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application
This survey categorizes agentic environments for LLMs by eight attributes and domains, introduces symbolic and neural synthesis paradigms with evaluation, and outlines four agent evolution pathways plus three environment evolution paradigms.
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Robots Need More than VLA and World Models
The paper identifies four missing interfaces (data autolabelling, embodiment retargeting, physics-grounded world models, and video-based reward inference) as the central bottleneck beyond VLA scaling for robot intelligence.
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Strategic Heterogeneous Multi-Agent Architecture for Cost-Effective Code Vulnerability Detection
A game-theoretic heterogeneous multi-agent architecture with three cloud LLMs and a local verifier achieves 77.2% F1, 100% recall, and 3x speedup for code vulnerability detection at $0.002 per sample on the NIST Juliet suite.
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Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework
Proposes a symbolic feedback-driven iterative self-refinement framework for LLM long-horizon planning that maps symbols to natural language, uses a verifier for error correction, and a plan recognizer for goal reachability, with abstract-level claims of improved feasibility and correctness.