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.
Tales: Text adventure learning environment suite
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Preference-based trajectory evaluation reduces tied comparisons from roughly 75% to 35% across agentic benchmarks by using temporal preferences over progress and return profiles.
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.
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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Offline Preference-Based Trajectory Evaluation
Preference-based trajectory evaluation reduces tied comparisons from roughly 75% to 35% across agentic benchmarks by using temporal preferences over progress and return profiles.
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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.
- DORA Explorer: Improving the Exploration Ability of LLMs Without Training