WebGameBench is a new benchmark that evaluates coding agents on building browser-native games from frozen specifications, with runtime browser evaluation showing best agents reach 76.9% usable rate but only 20.2% excellent rate.
Textworld: A learning environment for text-based games
8 Pith papers cite this work. Polarity classification is still indexing.
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BERT stores relational knowledge extractable via cloze queries without fine-tuning and matches supervised baselines on open-domain QA tasks.
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%.
MISE proves that hindsight self-evaluation rewards equal minimizing mutual information plus KL divergence to a proxy policy, and experiments show 7B LLMs reaching GPT-4o-level results on validation tasks.
MATM is a retrieval framework that lets populations of LLM agents share and reuse task trajectories to improve performance on interactive tasks without joint training.
LACE enables concurrent reasoning paths in LLMs to interact via lattice attention and a synthetic training pipeline, raising accuracy more than 7 points over independent parallel search.
Introduces GRIT, LTMI, and a hierarchical attention framework claiming performance gains on image captioning, visual dialog, and ALFRED instruction following.
A rationale is presented for developing an assistant in Minecraft to advance natural language understanding and dialogue learning.
citing papers explorer
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WebGameBench: Requirement-to-Application Evaluation for Coding Agents via Browser-Native Games
WebGameBench is a new benchmark that evaluates coding agents on building browser-native games from frozen specifications, with runtime browser evaluation showing best agents reach 76.9% usable rate but only 20.2% excellent rate.
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Language Models as Knowledge Bases?
BERT stores relational knowledge extractable via cloze queries without fine-tuning and matches supervised baselines on open-domain QA tasks.
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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%.
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Utilizing and Calibrating Hindsight Process Rewards via Reinforcement with Mutual Information Self-Evaluation
MISE proves that hindsight self-evaluation rewards equal minimizing mutual information plus KL divergence to a proxy policy, and experiments show 7B LLMs reaching GPT-4o-level results on validation tasks.
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Multi-Agent Transactive Memory
MATM is a retrieval framework that lets populations of LLM agents share and reuse task trajectories to improve performance on interactive tasks without joint training.
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LACE: Lattice Attention for Cross-thread Exploration
LACE enables concurrent reasoning paths in LLMs to interact via lattice attention and a synthetic training pipeline, raising accuracy more than 7 points over independent parallel search.
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Machine Intelligence that Understands Visual and Linguistic Information and Interacts with Humans and Environments
Introduces GRIT, LTMI, and a hierarchical attention framework claiming performance gains on image captioning, visual dialog, and ALFRED instruction following.
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Why Build an Assistant in Minecraft?
A rationale is presented for developing an assistant in Minecraft to advance natural language understanding and dialogue learning.